{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e9959ba9",
   "metadata": {},
   "source": [
    "# ShuffleNet 模型关键创新点总结\n",
    "\n",
    "## 1. 核心创新操作\n",
    "- **Pointwise Group Convolution（分组逐点卷积）**  \n",
    "  通过将逐点卷积（1x1卷积）操作分组处理，显著降低计算量，同时保持特征表达能力。\n",
    "\n",
    "- **Channel Shuffle（通道混洗）**  \n",
    "  在分组卷积后引入通道混洗操作，增强不同组之间的信息交互，避免因分组导致的特征表达能力下降。\n",
    "\n",
    "\n",
    "<img src=\"resources/shufflenet_v1_illustration.png\" alt=\"drawing\" width=\"50%\"/>\n",
    "\n",
    "## 2. 轻量化设计思路\n",
    "- **结合深度可分离卷积（Depthwise Separable Convolution）**  \n",
    "  在瓶颈层（bottleneck layer）中融合深度可分离卷积，进一步减少计算成本，适用于移动端低算力场景。\n",
    "\n",
    "<img src=\"resources/shufflenet_v1_block.png\" alt=\"drawing\" width=\"75%\"/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a3312ad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "999a0aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.dataset.imagenette_in_memory import ImagenetteInMemory\n",
    "from hdd.data_util.auto_augmentation import ImageNetPolicy\n",
    "\n",
    "from hdd.data_util.transforms import RandomResize\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "TRAIN_MEAN = [0.4625, 0.4580, 0.4295]\n",
    "TRAIN_STD = [0.2452, 0.2390, 0.2469]\n",
    "train_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        RandomResize([256, 296, 384]),  # 随机在三个size中选择一个进行resize\n",
    "        transforms.RandomCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        ImageNetPolicy(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "val_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "train_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"train\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=train_dataset_transforms,\n",
    ")\n",
    "val_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"val\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=val_dataset_transforms,\n",
    ")\n",
    "\n",
    "\n",
    "def build_dataloader(batch_size, train_dataset, val_dataset):\n",
    "    train_dataloader = DataLoader(\n",
    "        train_dataset, batch_size=batch_size, shuffle=True, num_workers=8\n",
    "    )\n",
    "    val_dataloader = DataLoader(\n",
    "        val_dataset, batch_size=batch_size, shuffle=False, num_workers=8\n",
    "    )\n",
    "    return train_dataloader, val_dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3bf1a0eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 24, 112, 112]             648\n",
      "       BatchNorm2d-2         [-1, 24, 112, 112]              48\n",
      "              ReLU-3         [-1, 24, 112, 112]               0\n",
      "           _Conv2d-4         [-1, 24, 112, 112]               0\n",
      "         MaxPool2d-5           [-1, 24, 56, 56]               0\n",
      "            Conv2d-6          [-1, 192, 28, 28]          41,472\n",
      "       BatchNorm2d-7          [-1, 192, 28, 28]             384\n",
      "           _Conv2d-8          [-1, 192, 28, 28]               0\n",
      "            Conv2d-9           [-1, 96, 56, 56]             288\n",
      "      BatchNorm2d-10           [-1, 96, 56, 56]             192\n",
      "             ReLU-11           [-1, 96, 56, 56]               0\n",
      "          _Conv2d-12           [-1, 96, 56, 56]               0\n",
      "        Rearrange-13           [-1, 96, 56, 56]               0\n",
      "           Conv2d-14           [-1, 96, 28, 28]             864\n",
      "      BatchNorm2d-15           [-1, 96, 28, 28]             192\n",
      "          _Conv2d-16           [-1, 96, 28, 28]               0\n",
      "           Conv2d-17          [-1, 192, 28, 28]           2,304\n",
      "      BatchNorm2d-18          [-1, 192, 28, 28]             384\n",
      "          _Conv2d-19          [-1, 192, 28, 28]               0\n",
      "  ShuffleNetBlock-20          [-1, 384, 28, 28]               0\n",
      "         Identity-21          [-1, 384, 28, 28]               0\n",
      "           Conv2d-22           [-1, 96, 28, 28]           4,608\n",
      "      BatchNorm2d-23           [-1, 96, 28, 28]             192\n",
      "             ReLU-24           [-1, 96, 28, 28]               0\n",
      "          _Conv2d-25           [-1, 96, 28, 28]               0\n",
      "        Rearrange-26           [-1, 96, 28, 28]               0\n",
      "           Conv2d-27           [-1, 96, 28, 28]             864\n",
      "      BatchNorm2d-28           [-1, 96, 28, 28]             192\n",
      "          _Conv2d-29           [-1, 96, 28, 28]               0\n",
      "           Conv2d-30          [-1, 384, 28, 28]           4,608\n",
      "      BatchNorm2d-31          [-1, 384, 28, 28]             768\n",
      "          _Conv2d-32          [-1, 384, 28, 28]               0\n",
      "  ShuffleNetBlock-33          [-1, 384, 28, 28]               0\n",
      "         Identity-34          [-1, 384, 28, 28]               0\n",
      "           Conv2d-35           [-1, 96, 28, 28]           4,608\n",
      "      BatchNorm2d-36           [-1, 96, 28, 28]             192\n",
      "             ReLU-37           [-1, 96, 28, 28]               0\n",
      "          _Conv2d-38           [-1, 96, 28, 28]               0\n",
      "        Rearrange-39           [-1, 96, 28, 28]               0\n",
      "           Conv2d-40           [-1, 96, 28, 28]             864\n",
      "      BatchNorm2d-41           [-1, 96, 28, 28]             192\n",
      "          _Conv2d-42           [-1, 96, 28, 28]               0\n",
      "           Conv2d-43          [-1, 384, 28, 28]           4,608\n",
      "      BatchNorm2d-44          [-1, 384, 28, 28]             768\n",
      "          _Conv2d-45          [-1, 384, 28, 28]               0\n",
      "  ShuffleNetBlock-46          [-1, 384, 28, 28]               0\n",
      "         Identity-47          [-1, 384, 28, 28]               0\n",
      "           Conv2d-48           [-1, 96, 28, 28]           4,608\n",
      "      BatchNorm2d-49           [-1, 96, 28, 28]             192\n",
      "             ReLU-50           [-1, 96, 28, 28]               0\n",
      "          _Conv2d-51           [-1, 96, 28, 28]               0\n",
      "        Rearrange-52           [-1, 96, 28, 28]               0\n",
      "           Conv2d-53           [-1, 96, 28, 28]             864\n",
      "      BatchNorm2d-54           [-1, 96, 28, 28]             192\n",
      "          _Conv2d-55           [-1, 96, 28, 28]               0\n",
      "           Conv2d-56          [-1, 384, 28, 28]           4,608\n",
      "      BatchNorm2d-57          [-1, 384, 28, 28]             768\n",
      "          _Conv2d-58          [-1, 384, 28, 28]               0\n",
      "  ShuffleNetBlock-59          [-1, 384, 28, 28]               0\n",
      "           Conv2d-60          [-1, 384, 14, 14]       1,327,104\n",
      "      BatchNorm2d-61          [-1, 384, 14, 14]             768\n",
      "          _Conv2d-62          [-1, 384, 14, 14]               0\n",
      "           Conv2d-63          [-1, 192, 28, 28]           9,216\n",
      "      BatchNorm2d-64          [-1, 192, 28, 28]             384\n",
      "             ReLU-65          [-1, 192, 28, 28]               0\n",
      "          _Conv2d-66          [-1, 192, 28, 28]               0\n",
      "        Rearrange-67          [-1, 192, 28, 28]               0\n",
      "           Conv2d-68          [-1, 192, 14, 14]           1,728\n",
      "      BatchNorm2d-69          [-1, 192, 14, 14]             384\n",
      "          _Conv2d-70          [-1, 192, 14, 14]               0\n",
      "           Conv2d-71          [-1, 384, 14, 14]           9,216\n",
      "      BatchNorm2d-72          [-1, 384, 14, 14]             768\n",
      "          _Conv2d-73          [-1, 384, 14, 14]               0\n",
      "  ShuffleNetBlock-74          [-1, 768, 14, 14]               0\n",
      "         Identity-75          [-1, 768, 14, 14]               0\n",
      "           Conv2d-76          [-1, 192, 14, 14]          18,432\n",
      "      BatchNorm2d-77          [-1, 192, 14, 14]             384\n",
      "             ReLU-78          [-1, 192, 14, 14]               0\n",
      "          _Conv2d-79          [-1, 192, 14, 14]               0\n",
      "        Rearrange-80          [-1, 192, 14, 14]               0\n",
      "           Conv2d-81          [-1, 192, 14, 14]           1,728\n",
      "      BatchNorm2d-82          [-1, 192, 14, 14]             384\n",
      "          _Conv2d-83          [-1, 192, 14, 14]               0\n",
      "           Conv2d-84          [-1, 768, 14, 14]          18,432\n",
      "      BatchNorm2d-85          [-1, 768, 14, 14]           1,536\n",
      "          _Conv2d-86          [-1, 768, 14, 14]               0\n",
      "  ShuffleNetBlock-87          [-1, 768, 14, 14]               0\n",
      "         Identity-88          [-1, 768, 14, 14]               0\n",
      "           Conv2d-89          [-1, 192, 14, 14]          18,432\n",
      "      BatchNorm2d-90          [-1, 192, 14, 14]             384\n",
      "             ReLU-91          [-1, 192, 14, 14]               0\n",
      "          _Conv2d-92          [-1, 192, 14, 14]               0\n",
      "        Rearrange-93          [-1, 192, 14, 14]               0\n",
      "           Conv2d-94          [-1, 192, 14, 14]           1,728\n",
      "      BatchNorm2d-95          [-1, 192, 14, 14]             384\n",
      "          _Conv2d-96          [-1, 192, 14, 14]               0\n",
      "           Conv2d-97          [-1, 768, 14, 14]          18,432\n",
      "      BatchNorm2d-98          [-1, 768, 14, 14]           1,536\n",
      "          _Conv2d-99          [-1, 768, 14, 14]               0\n",
      " ShuffleNetBlock-100          [-1, 768, 14, 14]               0\n",
      "        Identity-101          [-1, 768, 14, 14]               0\n",
      "          Conv2d-102          [-1, 192, 14, 14]          18,432\n",
      "     BatchNorm2d-103          [-1, 192, 14, 14]             384\n",
      "            ReLU-104          [-1, 192, 14, 14]               0\n",
      "         _Conv2d-105          [-1, 192, 14, 14]               0\n",
      "       Rearrange-106          [-1, 192, 14, 14]               0\n",
      "          Conv2d-107          [-1, 192, 14, 14]           1,728\n",
      "     BatchNorm2d-108          [-1, 192, 14, 14]             384\n",
      "         _Conv2d-109          [-1, 192, 14, 14]               0\n",
      "          Conv2d-110          [-1, 768, 14, 14]          18,432\n",
      "     BatchNorm2d-111          [-1, 768, 14, 14]           1,536\n",
      "         _Conv2d-112          [-1, 768, 14, 14]               0\n",
      " ShuffleNetBlock-113          [-1, 768, 14, 14]               0\n",
      "        Identity-114          [-1, 768, 14, 14]               0\n",
      "          Conv2d-115          [-1, 192, 14, 14]          18,432\n",
      "     BatchNorm2d-116          [-1, 192, 14, 14]             384\n",
      "            ReLU-117          [-1, 192, 14, 14]               0\n",
      "         _Conv2d-118          [-1, 192, 14, 14]               0\n",
      "       Rearrange-119          [-1, 192, 14, 14]               0\n",
      "          Conv2d-120          [-1, 192, 14, 14]           1,728\n",
      "     BatchNorm2d-121          [-1, 192, 14, 14]             384\n",
      "         _Conv2d-122          [-1, 192, 14, 14]               0\n",
      "          Conv2d-123          [-1, 768, 14, 14]          18,432\n",
      "     BatchNorm2d-124          [-1, 768, 14, 14]           1,536\n",
      "         _Conv2d-125          [-1, 768, 14, 14]               0\n",
      " ShuffleNetBlock-126          [-1, 768, 14, 14]               0\n",
      "        Identity-127          [-1, 768, 14, 14]               0\n",
      "          Conv2d-128          [-1, 192, 14, 14]          18,432\n",
      "     BatchNorm2d-129          [-1, 192, 14, 14]             384\n",
      "            ReLU-130          [-1, 192, 14, 14]               0\n",
      "         _Conv2d-131          [-1, 192, 14, 14]               0\n",
      "       Rearrange-132          [-1, 192, 14, 14]               0\n",
      "          Conv2d-133          [-1, 192, 14, 14]           1,728\n",
      "     BatchNorm2d-134          [-1, 192, 14, 14]             384\n",
      "         _Conv2d-135          [-1, 192, 14, 14]               0\n",
      "          Conv2d-136          [-1, 768, 14, 14]          18,432\n",
      "     BatchNorm2d-137          [-1, 768, 14, 14]           1,536\n",
      "         _Conv2d-138          [-1, 768, 14, 14]               0\n",
      " ShuffleNetBlock-139          [-1, 768, 14, 14]               0\n",
      "        Identity-140          [-1, 768, 14, 14]               0\n",
      "          Conv2d-141          [-1, 192, 14, 14]          18,432\n",
      "     BatchNorm2d-142          [-1, 192, 14, 14]             384\n",
      "            ReLU-143          [-1, 192, 14, 14]               0\n",
      "         _Conv2d-144          [-1, 192, 14, 14]               0\n",
      "       Rearrange-145          [-1, 192, 14, 14]               0\n",
      "          Conv2d-146          [-1, 192, 14, 14]           1,728\n",
      "     BatchNorm2d-147          [-1, 192, 14, 14]             384\n",
      "         _Conv2d-148          [-1, 192, 14, 14]               0\n",
      "          Conv2d-149          [-1, 768, 14, 14]          18,432\n",
      "     BatchNorm2d-150          [-1, 768, 14, 14]           1,536\n",
      "         _Conv2d-151          [-1, 768, 14, 14]               0\n",
      " ShuffleNetBlock-152          [-1, 768, 14, 14]               0\n",
      "        Identity-153          [-1, 768, 14, 14]               0\n",
      "          Conv2d-154          [-1, 192, 14, 14]          18,432\n",
      "     BatchNorm2d-155          [-1, 192, 14, 14]             384\n",
      "            ReLU-156          [-1, 192, 14, 14]               0\n",
      "         _Conv2d-157          [-1, 192, 14, 14]               0\n",
      "       Rearrange-158          [-1, 192, 14, 14]               0\n",
      "          Conv2d-159          [-1, 192, 14, 14]           1,728\n",
      "     BatchNorm2d-160          [-1, 192, 14, 14]             384\n",
      "         _Conv2d-161          [-1, 192, 14, 14]               0\n",
      "          Conv2d-162          [-1, 768, 14, 14]          18,432\n",
      "     BatchNorm2d-163          [-1, 768, 14, 14]           1,536\n",
      "         _Conv2d-164          [-1, 768, 14, 14]               0\n",
      " ShuffleNetBlock-165          [-1, 768, 14, 14]               0\n",
      "          Conv2d-166            [-1, 768, 7, 7]       5,308,416\n",
      "     BatchNorm2d-167            [-1, 768, 7, 7]           1,536\n",
      "         _Conv2d-168            [-1, 768, 7, 7]               0\n",
      "          Conv2d-169          [-1, 384, 14, 14]          36,864\n",
      "     BatchNorm2d-170          [-1, 384, 14, 14]             768\n",
      "            ReLU-171          [-1, 384, 14, 14]               0\n",
      "         _Conv2d-172          [-1, 384, 14, 14]               0\n",
      "       Rearrange-173          [-1, 384, 14, 14]               0\n",
      "          Conv2d-174            [-1, 384, 7, 7]           3,456\n",
      "     BatchNorm2d-175            [-1, 384, 7, 7]             768\n",
      "         _Conv2d-176            [-1, 384, 7, 7]               0\n",
      "          Conv2d-177            [-1, 768, 7, 7]          36,864\n",
      "     BatchNorm2d-178            [-1, 768, 7, 7]           1,536\n",
      "         _Conv2d-179            [-1, 768, 7, 7]               0\n",
      " ShuffleNetBlock-180           [-1, 1536, 7, 7]               0\n",
      "        Identity-181           [-1, 1536, 7, 7]               0\n",
      "          Conv2d-182            [-1, 384, 7, 7]          73,728\n",
      "     BatchNorm2d-183            [-1, 384, 7, 7]             768\n",
      "            ReLU-184            [-1, 384, 7, 7]               0\n",
      "         _Conv2d-185            [-1, 384, 7, 7]               0\n",
      "       Rearrange-186            [-1, 384, 7, 7]               0\n",
      "          Conv2d-187            [-1, 384, 7, 7]           3,456\n",
      "     BatchNorm2d-188            [-1, 384, 7, 7]             768\n",
      "         _Conv2d-189            [-1, 384, 7, 7]               0\n",
      "          Conv2d-190           [-1, 1536, 7, 7]          73,728\n",
      "     BatchNorm2d-191           [-1, 1536, 7, 7]           3,072\n",
      "         _Conv2d-192           [-1, 1536, 7, 7]               0\n",
      " ShuffleNetBlock-193           [-1, 1536, 7, 7]               0\n",
      "        Identity-194           [-1, 1536, 7, 7]               0\n",
      "          Conv2d-195            [-1, 384, 7, 7]          73,728\n",
      "     BatchNorm2d-196            [-1, 384, 7, 7]             768\n",
      "            ReLU-197            [-1, 384, 7, 7]               0\n",
      "         _Conv2d-198            [-1, 384, 7, 7]               0\n",
      "       Rearrange-199            [-1, 384, 7, 7]               0\n",
      "          Conv2d-200            [-1, 384, 7, 7]           3,456\n",
      "     BatchNorm2d-201            [-1, 384, 7, 7]             768\n",
      "         _Conv2d-202            [-1, 384, 7, 7]               0\n",
      "          Conv2d-203           [-1, 1536, 7, 7]          73,728\n",
      "     BatchNorm2d-204           [-1, 1536, 7, 7]           3,072\n",
      "         _Conv2d-205           [-1, 1536, 7, 7]               0\n",
      " ShuffleNetBlock-206           [-1, 1536, 7, 7]               0\n",
      "        Identity-207           [-1, 1536, 7, 7]               0\n",
      "          Conv2d-208            [-1, 384, 7, 7]          73,728\n",
      "     BatchNorm2d-209            [-1, 384, 7, 7]             768\n",
      "            ReLU-210            [-1, 384, 7, 7]               0\n",
      "         _Conv2d-211            [-1, 384, 7, 7]               0\n",
      "       Rearrange-212            [-1, 384, 7, 7]               0\n",
      "          Conv2d-213            [-1, 384, 7, 7]           3,456\n",
      "     BatchNorm2d-214            [-1, 384, 7, 7]             768\n",
      "         _Conv2d-215            [-1, 384, 7, 7]               0\n",
      "          Conv2d-216           [-1, 1536, 7, 7]          73,728\n",
      "     BatchNorm2d-217           [-1, 1536, 7, 7]           3,072\n",
      "         _Conv2d-218           [-1, 1536, 7, 7]               0\n",
      " ShuffleNetBlock-219           [-1, 1536, 7, 7]               0\n",
      "AdaptiveAvgPool2d-220           [-1, 1536, 1, 1]               0\n",
      "         Flatten-221                 [-1, 1536]               0\n",
      "         Dropout-222                 [-1, 1536]               0\n",
      "          Linear-223                   [-1, 10]          15,370\n",
      "================================================================\n",
      "Total params: 7,588,450\n",
      "Trainable params: 7,588,450\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 165.55\n",
      "Params size (MB): 28.95\n",
      "Estimated Total Size (MB): 195.08\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "import torchsummary\n",
    "from hdd.models.cnn.shufflenetv1 import ShuffleNetV1\n",
    "\n",
    "net = ShuffleNetV1(\n",
    "    num_classes=10,\n",
    "    group=8,\n",
    "    width_multiplier=1,\n",
    "    dropout=0.5,\n",
    ").to(DEVICE)\n",
    "torchsummary.summary(net, (3, 224, 224))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "851e736e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 7588450\n",
      "Epoch: 1/200 Train Loss: 2.1347 Accuracy: 0.2534 Time: 12.15335  | Val Loss: 2.0681 Accuracy: 0.3572\n",
      "Epoch: 2/200 Train Loss: 1.7995 Accuracy: 0.3952 Time: 12.21192  | Val Loss: 1.6747 Accuracy: 0.4504\n",
      "Epoch: 3/200 Train Loss: 1.6586 Accuracy: 0.4452 Time: 12.18383  | Val Loss: 1.3061 Accuracy: 0.5674\n",
      "Epoch: 4/200 Train Loss: 1.5468 Accuracy: 0.4893 Time: 12.22919  | Val Loss: 1.2447 Accuracy: 0.5888\n",
      "Epoch: 5/200 Train Loss: 1.4453 Accuracy: 0.5208 Time: 12.22658  | Val Loss: 1.3383 Accuracy: 0.5946\n",
      "Epoch: 6/200 Train Loss: 1.3842 Accuracy: 0.5383 Time: 12.23240  | Val Loss: 1.1009 Accuracy: 0.6538\n",
      "Epoch: 7/200 Train Loss: 1.3151 Accuracy: 0.5735 Time: 12.17363  | Val Loss: 0.9334 Accuracy: 0.6994\n",
      "Epoch: 8/200 Train Loss: 1.2862 Accuracy: 0.5819 Time: 12.17213  | Val Loss: 1.0573 Accuracy: 0.6690\n",
      "Epoch: 9/200 Train Loss: 1.2413 Accuracy: 0.5947 Time: 12.30401  | Val Loss: 1.1632 Accuracy: 0.6548\n",
      "Epoch: 10/200 Train Loss: 1.1554 Accuracy: 0.6198 Time: 12.24594  | Val Loss: 0.8045 Accuracy: 0.7473\n",
      "Epoch: 11/200 Train Loss: 1.1284 Accuracy: 0.6339 Time: 12.18952  | Val Loss: 0.8322 Accuracy: 0.7264\n",
      "Epoch: 12/200 Train Loss: 1.0947 Accuracy: 0.6385 Time: 12.36493  | Val Loss: 0.7642 Accuracy: 0.7569\n",
      "Epoch: 13/200 Train Loss: 1.0436 Accuracy: 0.6628 Time: 12.26320  | Val Loss: 0.6518 Accuracy: 0.7911\n",
      "Epoch: 14/200 Train Loss: 1.0165 Accuracy: 0.6693 Time: 12.27968  | Val Loss: 0.8501 Accuracy: 0.7269\n",
      "Epoch: 15/200 Train Loss: 0.9876 Accuracy: 0.6799 Time: 12.33084  | Val Loss: 0.6867 Accuracy: 0.7763\n",
      "Epoch: 16/200 Train Loss: 0.9882 Accuracy: 0.6837 Time: 12.23879  | Val Loss: 0.6541 Accuracy: 0.7957\n",
      "Epoch: 17/200 Train Loss: 0.9488 Accuracy: 0.6913 Time: 12.25758  | Val Loss: 0.6480 Accuracy: 0.7946\n",
      "Epoch: 18/200 Train Loss: 0.9209 Accuracy: 0.6978 Time: 12.26918  | Val Loss: 0.6915 Accuracy: 0.7921\n",
      "Epoch: 19/200 Train Loss: 0.9168 Accuracy: 0.6998 Time: 12.22745  | Val Loss: 0.5635 Accuracy: 0.8148\n",
      "Epoch: 20/200 Train Loss: 0.8558 Accuracy: 0.7221 Time: 12.25414  | Val Loss: 0.5333 Accuracy: 0.8303\n",
      "Epoch: 21/200 Train Loss: 0.8636 Accuracy: 0.7228 Time: 12.26312  | Val Loss: 0.5648 Accuracy: 0.8194\n",
      "Epoch: 22/200 Train Loss: 0.8585 Accuracy: 0.7234 Time: 12.10559  | Val Loss: 0.6378 Accuracy: 0.7977\n",
      "Epoch: 23/200 Train Loss: 0.7985 Accuracy: 0.7432 Time: 12.16578  | Val Loss: 0.5517 Accuracy: 0.8262\n",
      "Epoch: 24/200 Train Loss: 0.8087 Accuracy: 0.7320 Time: 12.08447  | Val Loss: 0.6474 Accuracy: 0.8028\n",
      "Epoch: 25/200 Train Loss: 0.7967 Accuracy: 0.7438 Time: 12.23865  | Val Loss: 0.5862 Accuracy: 0.8219\n",
      "Epoch: 26/200 Train Loss: 0.7685 Accuracy: 0.7530 Time: 12.24053  | Val Loss: 0.5144 Accuracy: 0.8405\n",
      "Epoch: 27/200 Train Loss: 0.7756 Accuracy: 0.7451 Time: 12.29283  | Val Loss: 0.5176 Accuracy: 0.8359\n",
      "Epoch: 28/200 Train Loss: 0.7477 Accuracy: 0.7567 Time: 12.28035  | Val Loss: 0.5100 Accuracy: 0.8382\n",
      "Epoch: 29/200 Train Loss: 0.7408 Accuracy: 0.7568 Time: 12.24873  | Val Loss: 0.5088 Accuracy: 0.8349\n",
      "Epoch: 30/200 Train Loss: 0.7505 Accuracy: 0.7546 Time: 12.32186  | Val Loss: 0.5498 Accuracy: 0.8280\n",
      "Epoch: 31/200 Train Loss: 0.7172 Accuracy: 0.7691 Time: 12.21872  | Val Loss: 0.5494 Accuracy: 0.8250\n",
      "Epoch: 32/200 Train Loss: 0.7232 Accuracy: 0.7607 Time: 12.21618  | Val Loss: 0.4887 Accuracy: 0.8451\n",
      "Epoch: 33/200 Train Loss: 0.6976 Accuracy: 0.7724 Time: 12.20088  | Val Loss: 0.4442 Accuracy: 0.8535\n",
      "Epoch: 34/200 Train Loss: 0.6663 Accuracy: 0.7805 Time: 12.13961  | Val Loss: 0.4546 Accuracy: 0.8535\n",
      "Epoch: 35/200 Train Loss: 0.6824 Accuracy: 0.7820 Time: 12.15357  | Val Loss: 0.5020 Accuracy: 0.8385\n",
      "Epoch: 36/200 Train Loss: 0.6559 Accuracy: 0.7858 Time: 12.17947  | Val Loss: 0.4803 Accuracy: 0.8418\n",
      "Epoch: 37/200 Train Loss: 0.6529 Accuracy: 0.7874 Time: 12.15002  | Val Loss: 0.4893 Accuracy: 0.8446\n",
      "Epoch: 38/200 Train Loss: 0.6541 Accuracy: 0.7883 Time: 12.03102  | Val Loss: 0.4936 Accuracy: 0.8451\n",
      "Epoch: 39/200 Train Loss: 0.6530 Accuracy: 0.7849 Time: 11.99363  | Val Loss: 0.4260 Accuracy: 0.8639\n",
      "Epoch: 40/200 Train Loss: 0.6295 Accuracy: 0.7901 Time: 12.00669  | Val Loss: 0.4670 Accuracy: 0.8520\n",
      "Epoch: 41/200 Train Loss: 0.6125 Accuracy: 0.8036 Time: 12.08737  | Val Loss: 0.4184 Accuracy: 0.8675\n",
      "Epoch: 42/200 Train Loss: 0.6250 Accuracy: 0.7965 Time: 12.19640  | Val Loss: 0.4324 Accuracy: 0.8614\n",
      "Epoch: 43/200 Train Loss: 0.6242 Accuracy: 0.7996 Time: 12.23069  | Val Loss: 0.4224 Accuracy: 0.8662\n",
      "Epoch: 44/200 Train Loss: 0.6009 Accuracy: 0.8026 Time: 12.27039  | Val Loss: 0.4011 Accuracy: 0.8741\n",
      "Epoch: 45/200 Train Loss: 0.5917 Accuracy: 0.8071 Time: 12.23483  | Val Loss: 0.4659 Accuracy: 0.8502\n",
      "Epoch: 46/200 Train Loss: 0.5852 Accuracy: 0.8080 Time: 12.19378  | Val Loss: 0.4184 Accuracy: 0.8762\n",
      "Epoch: 47/200 Train Loss: 0.5907 Accuracy: 0.8115 Time: 12.15891  | Val Loss: 0.4384 Accuracy: 0.8617\n",
      "Epoch: 48/200 Train Loss: 0.5428 Accuracy: 0.8213 Time: 12.19038  | Val Loss: 0.4243 Accuracy: 0.8660\n",
      "Epoch: 49/200 Train Loss: 0.5754 Accuracy: 0.8118 Time: 12.10726  | Val Loss: 0.4353 Accuracy: 0.8647\n",
      "Epoch: 50/200 Train Loss: 0.5680 Accuracy: 0.8130 Time: 12.20403  | Val Loss: 0.4167 Accuracy: 0.8731\n",
      "Epoch: 51/200 Train Loss: 0.5418 Accuracy: 0.8207 Time: 12.27473  | Val Loss: 0.4115 Accuracy: 0.8729\n",
      "Epoch: 52/200 Train Loss: 0.5439 Accuracy: 0.8222 Time: 12.18322  | Val Loss: 0.4217 Accuracy: 0.8668\n",
      "Epoch: 53/200 Train Loss: 0.5541 Accuracy: 0.8203 Time: 12.05063  | Val Loss: 0.4136 Accuracy: 0.8645\n",
      "Epoch: 54/200 Train Loss: 0.5367 Accuracy: 0.8231 Time: 12.03947  | Val Loss: 0.3954 Accuracy: 0.8736\n",
      "Epoch: 55/200 Train Loss: 0.5333 Accuracy: 0.8276 Time: 11.99805  | Val Loss: 0.3858 Accuracy: 0.8775\n",
      "Epoch: 56/200 Train Loss: 0.5320 Accuracy: 0.8294 Time: 11.97393  | Val Loss: 0.3815 Accuracy: 0.8810\n",
      "Epoch: 57/200 Train Loss: 0.5268 Accuracy: 0.8303 Time: 12.14389  | Val Loss: 0.4097 Accuracy: 0.8703\n",
      "Epoch: 58/200 Train Loss: 0.5045 Accuracy: 0.8342 Time: 11.89523  | Val Loss: 0.3620 Accuracy: 0.8876\n",
      "Epoch: 59/200 Train Loss: 0.5165 Accuracy: 0.8331 Time: 11.84991  | Val Loss: 0.3729 Accuracy: 0.8803\n",
      "Epoch: 60/200 Train Loss: 0.5019 Accuracy: 0.8356 Time: 11.88526  | Val Loss: 0.3857 Accuracy: 0.8813\n",
      "Epoch: 61/200 Train Loss: 0.5081 Accuracy: 0.8334 Time: 11.88162  | Val Loss: 0.4122 Accuracy: 0.8685\n",
      "Epoch: 62/200 Train Loss: 0.4915 Accuracy: 0.8376 Time: 11.84614  | Val Loss: 0.3413 Accuracy: 0.8879\n",
      "Epoch: 63/200 Train Loss: 0.4695 Accuracy: 0.8472 Time: 11.94497  | Val Loss: 0.3628 Accuracy: 0.8808\n",
      "Epoch: 64/200 Train Loss: 0.4830 Accuracy: 0.8441 Time: 11.84408  | Val Loss: 0.3642 Accuracy: 0.8851\n",
      "Epoch: 65/200 Train Loss: 0.4674 Accuracy: 0.8493 Time: 11.83589  | Val Loss: 0.3622 Accuracy: 0.8866\n",
      "Epoch: 66/200 Train Loss: 0.4835 Accuracy: 0.8431 Time: 11.81129  | Val Loss: 0.3627 Accuracy: 0.8854\n",
      "Epoch: 67/200 Train Loss: 0.4649 Accuracy: 0.8485 Time: 11.76586  | Val Loss: 0.4289 Accuracy: 0.8668\n",
      "Epoch: 68/200 Train Loss: 0.4647 Accuracy: 0.8472 Time: 11.79028  | Val Loss: 0.3932 Accuracy: 0.8790\n",
      "Epoch: 69/200 Train Loss: 0.4487 Accuracy: 0.8537 Time: 11.80062  | Val Loss: 0.3619 Accuracy: 0.8882\n",
      "Epoch: 70/200 Train Loss: 0.4594 Accuracy: 0.8470 Time: 11.87438  | Val Loss: 0.3600 Accuracy: 0.8899\n",
      "Epoch: 71/200 Train Loss: 0.4448 Accuracy: 0.8571 Time: 11.83149  | Val Loss: 0.3642 Accuracy: 0.8848\n",
      "Epoch: 72/200 Train Loss: 0.4439 Accuracy: 0.8527 Time: 11.81269  | Val Loss: 0.3871 Accuracy: 0.8813\n",
      "Epoch: 73/200 Train Loss: 0.4442 Accuracy: 0.8533 Time: 11.85590  | Val Loss: 0.3688 Accuracy: 0.8813\n",
      "Epoch: 74/200 Train Loss: 0.4393 Accuracy: 0.8525 Time: 11.84247  | Val Loss: 0.3382 Accuracy: 0.8971\n",
      "Epoch: 75/200 Train Loss: 0.4375 Accuracy: 0.8580 Time: 11.85672  | Val Loss: 0.3464 Accuracy: 0.8958\n",
      "Epoch: 76/200 Train Loss: 0.4128 Accuracy: 0.8625 Time: 11.78116  | Val Loss: 0.3648 Accuracy: 0.8859\n",
      "Epoch: 77/200 Train Loss: 0.4281 Accuracy: 0.8590 Time: 11.76265  | Val Loss: 0.3477 Accuracy: 0.8904\n",
      "Epoch: 78/200 Train Loss: 0.4153 Accuracy: 0.8628 Time: 11.82450  | Val Loss: 0.3436 Accuracy: 0.8973\n",
      "Epoch: 79/200 Train Loss: 0.4002 Accuracy: 0.8687 Time: 11.81915  | Val Loss: 0.3476 Accuracy: 0.8950\n",
      "Epoch: 80/200 Train Loss: 0.4072 Accuracy: 0.8668 Time: 11.74129  | Val Loss: 0.3595 Accuracy: 0.8899\n",
      "Epoch: 81/200 Train Loss: 0.3962 Accuracy: 0.8722 Time: 11.76055  | Val Loss: 0.3504 Accuracy: 0.8950\n",
      "Epoch: 82/200 Train Loss: 0.3845 Accuracy: 0.8768 Time: 11.85626  | Val Loss: 0.3535 Accuracy: 0.8920\n",
      "Epoch: 83/200 Train Loss: 0.3829 Accuracy: 0.8759 Time: 11.85118  | Val Loss: 0.3382 Accuracy: 0.8950\n",
      "Epoch: 84/200 Train Loss: 0.3980 Accuracy: 0.8687 Time: 11.80545  | Val Loss: 0.3711 Accuracy: 0.8851\n",
      "Epoch: 85/200 Train Loss: 0.3953 Accuracy: 0.8744 Time: 11.80621  | Val Loss: 0.3167 Accuracy: 0.9022\n",
      "Epoch: 86/200 Train Loss: 0.3857 Accuracy: 0.8752 Time: 11.80069  | Val Loss: 0.3678 Accuracy: 0.8958\n",
      "Epoch: 87/200 Train Loss: 0.3835 Accuracy: 0.8740 Time: 11.82669  | Val Loss: 0.3289 Accuracy: 0.9006\n",
      "Epoch: 88/200 Train Loss: 0.3756 Accuracy: 0.8770 Time: 11.84147  | Val Loss: 0.3379 Accuracy: 0.9019\n",
      "Epoch: 89/200 Train Loss: 0.3745 Accuracy: 0.8753 Time: 11.84659  | Val Loss: 0.3122 Accuracy: 0.9088\n",
      "Epoch: 90/200 Train Loss: 0.3772 Accuracy: 0.8762 Time: 11.82814  | Val Loss: 0.3165 Accuracy: 0.9073\n",
      "Epoch: 91/200 Train Loss: 0.3638 Accuracy: 0.8816 Time: 11.87261  | Val Loss: 0.3254 Accuracy: 0.9037\n",
      "Epoch: 92/200 Train Loss: 0.3513 Accuracy: 0.8837 Time: 11.84551  | Val Loss: 0.3141 Accuracy: 0.9034\n",
      "Epoch: 93/200 Train Loss: 0.3523 Accuracy: 0.8857 Time: 11.83525  | Val Loss: 0.3662 Accuracy: 0.8894\n",
      "Epoch: 94/200 Train Loss: 0.3561 Accuracy: 0.8866 Time: 11.77491  | Val Loss: 0.3436 Accuracy: 0.8996\n",
      "Epoch: 95/200 Train Loss: 0.3532 Accuracy: 0.8819 Time: 11.87029  | Val Loss: 0.3311 Accuracy: 0.9022\n",
      "Epoch: 96/200 Train Loss: 0.3491 Accuracy: 0.8875 Time: 11.78251  | Val Loss: 0.3373 Accuracy: 0.8983\n",
      "Epoch: 97/200 Train Loss: 0.3501 Accuracy: 0.8843 Time: 11.79621  | Val Loss: 0.3542 Accuracy: 0.8961\n",
      "Epoch: 98/200 Train Loss: 0.3504 Accuracy: 0.8847 Time: 11.80808  | Val Loss: 0.3281 Accuracy: 0.8989\n",
      "Epoch: 99/200 Train Loss: 0.3412 Accuracy: 0.8856 Time: 11.83585  | Val Loss: 0.3186 Accuracy: 0.9050\n",
      "Epoch: 100/200 Train Loss: 0.3495 Accuracy: 0.8888 Time: 12.01194  | Val Loss: 0.3205 Accuracy: 0.9060\n",
      "Epoch: 101/200 Train Loss: 0.3279 Accuracy: 0.8912 Time: 12.25951  | Val Loss: 0.3128 Accuracy: 0.9085\n",
      "Epoch: 102/200 Train Loss: 0.3236 Accuracy: 0.8926 Time: 12.25306  | Val Loss: 0.3132 Accuracy: 0.9055\n",
      "Epoch: 103/200 Train Loss: 0.3140 Accuracy: 0.8987 Time: 12.34162  | Val Loss: 0.3090 Accuracy: 0.9060\n",
      "Epoch: 104/200 Train Loss: 0.3099 Accuracy: 0.8962 Time: 12.22441  | Val Loss: 0.3270 Accuracy: 0.9045\n",
      "Epoch: 105/200 Train Loss: 0.3046 Accuracy: 0.8999 Time: 12.17286  | Val Loss: 0.3209 Accuracy: 0.9017\n",
      "Epoch: 106/200 Train Loss: 0.3181 Accuracy: 0.8970 Time: 12.01224  | Val Loss: 0.3193 Accuracy: 0.9024\n",
      "Epoch: 107/200 Train Loss: 0.3054 Accuracy: 0.8990 Time: 11.98751  | Val Loss: 0.3005 Accuracy: 0.9080\n",
      "Epoch: 108/200 Train Loss: 0.3108 Accuracy: 0.8982 Time: 11.98756  | Val Loss: 0.3062 Accuracy: 0.9124\n",
      "Epoch: 109/200 Train Loss: 0.2946 Accuracy: 0.9013 Time: 12.07642  | Val Loss: 0.3188 Accuracy: 0.9078\n",
      "Epoch: 110/200 Train Loss: 0.3175 Accuracy: 0.8973 Time: 12.06863  | Val Loss: 0.3029 Accuracy: 0.9108\n",
      "Epoch: 111/200 Train Loss: 0.3041 Accuracy: 0.9010 Time: 12.04315  | Val Loss: 0.3059 Accuracy: 0.9045\n",
      "Epoch: 112/200 Train Loss: 0.2895 Accuracy: 0.9044 Time: 12.22150  | Val Loss: 0.3107 Accuracy: 0.9068\n",
      "Epoch: 113/200 Train Loss: 0.2909 Accuracy: 0.9022 Time: 12.22551  | Val Loss: 0.3111 Accuracy: 0.9085\n",
      "Epoch: 114/200 Train Loss: 0.2956 Accuracy: 0.8999 Time: 12.30539  | Val Loss: 0.3228 Accuracy: 0.9047\n",
      "Epoch: 115/200 Train Loss: 0.2809 Accuracy: 0.9075 Time: 12.12260  | Val Loss: 0.3162 Accuracy: 0.9050\n",
      "Epoch: 116/200 Train Loss: 0.2872 Accuracy: 0.9043 Time: 12.23573  | Val Loss: 0.3002 Accuracy: 0.9060\n",
      "Epoch: 117/200 Train Loss: 0.2827 Accuracy: 0.9083 Time: 12.23556  | Val Loss: 0.2905 Accuracy: 0.9093\n",
      "Epoch: 118/200 Train Loss: 0.2727 Accuracy: 0.9098 Time: 12.22717  | Val Loss: 0.2952 Accuracy: 0.9131\n",
      "Epoch: 119/200 Train Loss: 0.2740 Accuracy: 0.9117 Time: 12.26029  | Val Loss: 0.3167 Accuracy: 0.9052\n",
      "Epoch: 120/200 Train Loss: 0.2725 Accuracy: 0.9103 Time: 12.30452  | Val Loss: 0.2963 Accuracy: 0.9096\n",
      "Epoch: 121/200 Train Loss: 0.2656 Accuracy: 0.9128 Time: 12.13409  | Val Loss: 0.2983 Accuracy: 0.9162\n",
      "Epoch: 122/200 Train Loss: 0.2674 Accuracy: 0.9104 Time: 11.88822  | Val Loss: 0.3061 Accuracy: 0.9126\n",
      "Epoch: 123/200 Train Loss: 0.2662 Accuracy: 0.9113 Time: 11.82507  | Val Loss: 0.2911 Accuracy: 0.9101\n",
      "Epoch: 124/200 Train Loss: 0.2608 Accuracy: 0.9144 Time: 11.77783  | Val Loss: 0.2891 Accuracy: 0.9121\n",
      "Epoch: 125/200 Train Loss: 0.2601 Accuracy: 0.9164 Time: 11.79726  | Val Loss: 0.2952 Accuracy: 0.9121\n",
      "Epoch: 126/200 Train Loss: 0.2694 Accuracy: 0.9096 Time: 11.80476  | Val Loss: 0.2981 Accuracy: 0.9113\n",
      "Epoch: 127/200 Train Loss: 0.2411 Accuracy: 0.9213 Time: 11.77180  | Val Loss: 0.2867 Accuracy: 0.9129\n",
      "Epoch: 128/200 Train Loss: 0.2554 Accuracy: 0.9142 Time: 11.80448  | Val Loss: 0.2891 Accuracy: 0.9146\n",
      "Epoch: 129/200 Train Loss: 0.2491 Accuracy: 0.9179 Time: 11.87973  | Val Loss: 0.2992 Accuracy: 0.9136\n",
      "Epoch: 130/200 Train Loss: 0.2481 Accuracy: 0.9209 Time: 11.85464  | Val Loss: 0.2831 Accuracy: 0.9205\n",
      "Epoch: 131/200 Train Loss: 0.2607 Accuracy: 0.9111 Time: 11.83471  | Val Loss: 0.2868 Accuracy: 0.9134\n",
      "Epoch: 132/200 Train Loss: 0.2304 Accuracy: 0.9229 Time: 11.84124  | Val Loss: 0.2904 Accuracy: 0.9116\n",
      "Epoch: 133/200 Train Loss: 0.2437 Accuracy: 0.9201 Time: 11.83706  | Val Loss: 0.2982 Accuracy: 0.9139\n",
      "Epoch: 134/200 Train Loss: 0.2367 Accuracy: 0.9239 Time: 11.82911  | Val Loss: 0.2860 Accuracy: 0.9177\n",
      "Epoch: 135/200 Train Loss: 0.2344 Accuracy: 0.9232 Time: 11.82829  | Val Loss: 0.2896 Accuracy: 0.9116\n",
      "Epoch: 136/200 Train Loss: 0.2289 Accuracy: 0.9263 Time: 11.83461  | Val Loss: 0.2881 Accuracy: 0.9167\n",
      "Epoch: 137/200 Train Loss: 0.2280 Accuracy: 0.9252 Time: 11.80184  | Val Loss: 0.2939 Accuracy: 0.9146\n",
      "Epoch: 138/200 Train Loss: 0.2349 Accuracy: 0.9243 Time: 11.76231  | Val Loss: 0.2861 Accuracy: 0.9149\n",
      "Epoch: 139/200 Train Loss: 0.2235 Accuracy: 0.9271 Time: 11.90791  | Val Loss: 0.2925 Accuracy: 0.9134\n",
      "Epoch: 140/200 Train Loss: 0.2336 Accuracy: 0.9251 Time: 11.75686  | Val Loss: 0.2827 Accuracy: 0.9177\n",
      "Epoch: 141/200 Train Loss: 0.2276 Accuracy: 0.9263 Time: 11.79657  | Val Loss: 0.2691 Accuracy: 0.9246\n",
      "Epoch: 142/200 Train Loss: 0.2171 Accuracy: 0.9291 Time: 11.80217  | Val Loss: 0.2801 Accuracy: 0.9154\n",
      "Epoch: 143/200 Train Loss: 0.2176 Accuracy: 0.9274 Time: 11.77629  | Val Loss: 0.2804 Accuracy: 0.9208\n",
      "Epoch: 144/200 Train Loss: 0.2134 Accuracy: 0.9307 Time: 11.78064  | Val Loss: 0.2887 Accuracy: 0.9157\n",
      "Epoch: 145/200 Train Loss: 0.2159 Accuracy: 0.9295 Time: 11.81591  | Val Loss: 0.2905 Accuracy: 0.9172\n",
      "Epoch: 146/200 Train Loss: 0.2180 Accuracy: 0.9284 Time: 11.86828  | Val Loss: 0.2778 Accuracy: 0.9228\n",
      "Epoch: 147/200 Train Loss: 0.2144 Accuracy: 0.9303 Time: 11.85301  | Val Loss: 0.2897 Accuracy: 0.9169\n",
      "Epoch: 148/200 Train Loss: 0.2102 Accuracy: 0.9287 Time: 11.86141  | Val Loss: 0.2786 Accuracy: 0.9190\n",
      "Epoch: 149/200 Train Loss: 0.2068 Accuracy: 0.9299 Time: 11.83297  | Val Loss: 0.2851 Accuracy: 0.9172\n",
      "Epoch: 150/200 Train Loss: 0.2116 Accuracy: 0.9325 Time: 11.82586  | Val Loss: 0.2773 Accuracy: 0.9190\n",
      "Epoch: 151/200 Train Loss: 0.1979 Accuracy: 0.9340 Time: 11.82968  | Val Loss: 0.2820 Accuracy: 0.9157\n",
      "Epoch: 152/200 Train Loss: 0.2059 Accuracy: 0.9343 Time: 11.83449  | Val Loss: 0.2807 Accuracy: 0.9185\n",
      "Epoch: 153/200 Train Loss: 0.2022 Accuracy: 0.9353 Time: 11.82149  | Val Loss: 0.2863 Accuracy: 0.9172\n",
      "Epoch: 154/200 Train Loss: 0.2034 Accuracy: 0.9325 Time: 11.78809  | Val Loss: 0.2755 Accuracy: 0.9223\n",
      "Epoch: 155/200 Train Loss: 0.1981 Accuracy: 0.9387 Time: 11.83641  | Val Loss: 0.2749 Accuracy: 0.9225\n",
      "Epoch: 156/200 Train Loss: 0.2105 Accuracy: 0.9316 Time: 11.79195  | Val Loss: 0.2810 Accuracy: 0.9208\n",
      "Epoch: 157/200 Train Loss: 0.2032 Accuracy: 0.9323 Time: 11.75192  | Val Loss: 0.2785 Accuracy: 0.9197\n",
      "Epoch: 158/200 Train Loss: 0.1985 Accuracy: 0.9347 Time: 11.87867  | Val Loss: 0.2739 Accuracy: 0.9215\n",
      "Epoch: 159/200 Train Loss: 0.1876 Accuracy: 0.9406 Time: 11.86833  | Val Loss: 0.2712 Accuracy: 0.9210\n",
      "Epoch: 160/200 Train Loss: 0.2019 Accuracy: 0.9319 Time: 11.85930  | Val Loss: 0.2670 Accuracy: 0.9236\n",
      "Epoch: 161/200 Train Loss: 0.1898 Accuracy: 0.9351 Time: 11.86294  | Val Loss: 0.2627 Accuracy: 0.9243\n",
      "Epoch: 162/200 Train Loss: 0.1990 Accuracy: 0.9361 Time: 11.84194  | Val Loss: 0.2651 Accuracy: 0.9251\n",
      "Epoch: 163/200 Train Loss: 0.1884 Accuracy: 0.9390 Time: 11.91961  | Val Loss: 0.2608 Accuracy: 0.9243\n",
      "Epoch: 164/200 Train Loss: 0.1968 Accuracy: 0.9342 Time: 11.82045  | Val Loss: 0.2723 Accuracy: 0.9200\n",
      "Epoch: 165/200 Train Loss: 0.1856 Accuracy: 0.9393 Time: 11.76748  | Val Loss: 0.2741 Accuracy: 0.9203\n",
      "Epoch: 166/200 Train Loss: 0.1933 Accuracy: 0.9370 Time: 11.81372  | Val Loss: 0.2654 Accuracy: 0.9238\n",
      "Epoch: 167/200 Train Loss: 0.1849 Accuracy: 0.9422 Time: 11.78969  | Val Loss: 0.2718 Accuracy: 0.9233\n",
      "Epoch: 168/200 Train Loss: 0.1795 Accuracy: 0.9401 Time: 11.76483  | Val Loss: 0.2827 Accuracy: 0.9177\n",
      "Epoch: 169/200 Train Loss: 0.1923 Accuracy: 0.9387 Time: 11.76509  | Val Loss: 0.2711 Accuracy: 0.9192\n",
      "Epoch: 170/200 Train Loss: 0.1702 Accuracy: 0.9406 Time: 11.82243  | Val Loss: 0.2794 Accuracy: 0.9210\n",
      "Epoch: 171/200 Train Loss: 0.1872 Accuracy: 0.9403 Time: 11.77150  | Val Loss: 0.2684 Accuracy: 0.9246\n",
      "Epoch: 172/200 Train Loss: 0.1816 Accuracy: 0.9403 Time: 11.85997  | Val Loss: 0.2700 Accuracy: 0.9241\n",
      "Epoch: 173/200 Train Loss: 0.1955 Accuracy: 0.9366 Time: 11.81081  | Val Loss: 0.2674 Accuracy: 0.9220\n",
      "Epoch: 174/200 Train Loss: 0.1939 Accuracy: 0.9345 Time: 11.77456  | Val Loss: 0.2696 Accuracy: 0.9200\n",
      "Epoch: 175/200 Train Loss: 0.1872 Accuracy: 0.9405 Time: 11.77869  | Val Loss: 0.2671 Accuracy: 0.9231\n",
      "Epoch: 176/200 Train Loss: 0.1907 Accuracy: 0.9395 Time: 11.85543  | Val Loss: 0.2703 Accuracy: 0.9215\n",
      "Epoch: 177/200 Train Loss: 0.1728 Accuracy: 0.9413 Time: 11.89991  | Val Loss: 0.2661 Accuracy: 0.9220\n",
      "Epoch: 178/200 Train Loss: 0.1712 Accuracy: 0.9450 Time: 11.82897  | Val Loss: 0.2646 Accuracy: 0.9238\n",
      "Epoch: 179/200 Train Loss: 0.1852 Accuracy: 0.9383 Time: 11.83427  | Val Loss: 0.2652 Accuracy: 0.9238\n",
      "Epoch: 180/200 Train Loss: 0.1794 Accuracy: 0.9406 Time: 11.84977  | Val Loss: 0.2657 Accuracy: 0.9220\n",
      "Epoch: 181/200 Train Loss: 0.1637 Accuracy: 0.9481 Time: 11.83236  | Val Loss: 0.2644 Accuracy: 0.9225\n",
      "Epoch: 182/200 Train Loss: 0.1636 Accuracy: 0.9468 Time: 11.79044  | Val Loss: 0.2619 Accuracy: 0.9220\n",
      "Epoch: 183/200 Train Loss: 0.1783 Accuracy: 0.9411 Time: 11.78995  | Val Loss: 0.2622 Accuracy: 0.9220\n",
      "Epoch: 184/200 Train Loss: 0.1548 Accuracy: 0.9478 Time: 11.76203  | Val Loss: 0.2647 Accuracy: 0.9228\n",
      "Epoch: 185/200 Train Loss: 0.1739 Accuracy: 0.9458 Time: 11.76911  | Val Loss: 0.2668 Accuracy: 0.9231\n",
      "Epoch: 186/200 Train Loss: 0.1720 Accuracy: 0.9448 Time: 11.78392  | Val Loss: 0.2668 Accuracy: 0.9215\n",
      "Epoch: 187/200 Train Loss: 0.1638 Accuracy: 0.9487 Time: 11.79382  | Val Loss: 0.2683 Accuracy: 0.9220\n",
      "Epoch: 188/200 Train Loss: 0.1812 Accuracy: 0.9390 Time: 11.82526  | Val Loss: 0.2660 Accuracy: 0.9208\n",
      "Epoch: 189/200 Train Loss: 0.1723 Accuracy: 0.9421 Time: 11.81585  | Val Loss: 0.2668 Accuracy: 0.9205\n",
      "Epoch: 190/200 Train Loss: 0.1754 Accuracy: 0.9436 Time: 11.82214  | Val Loss: 0.2682 Accuracy: 0.9218\n",
      "Epoch: 191/200 Train Loss: 0.1774 Accuracy: 0.9435 Time: 11.82052  | Val Loss: 0.2676 Accuracy: 0.9223\n",
      "Epoch: 192/200 Train Loss: 0.1827 Accuracy: 0.9382 Time: 11.87718  | Val Loss: 0.2669 Accuracy: 0.9218\n",
      "Epoch: 193/200 Train Loss: 0.1768 Accuracy: 0.9425 Time: 11.78093  | Val Loss: 0.2657 Accuracy: 0.9205\n",
      "Epoch: 194/200 Train Loss: 0.1782 Accuracy: 0.9417 Time: 11.79185  | Val Loss: 0.2678 Accuracy: 0.9208\n",
      "Epoch: 195/200 Train Loss: 0.1790 Accuracy: 0.9411 Time: 11.79642  | Val Loss: 0.2648 Accuracy: 0.9215\n",
      "Epoch: 196/200 Train Loss: 0.1663 Accuracy: 0.9459 Time: 11.79969  | Val Loss: 0.2673 Accuracy: 0.9215\n",
      "Epoch: 197/200 Train Loss: 0.1810 Accuracy: 0.9373 Time: 11.91799  | Val Loss: 0.2620 Accuracy: 0.9223\n",
      "Epoch: 198/200 Train Loss: 0.1715 Accuracy: 0.9459 Time: 11.79762  | Val Loss: 0.2653 Accuracy: 0.9205\n",
      "Epoch: 199/200 Train Loss: 0.1728 Accuracy: 0.9446 Time: 11.80670  | Val Loss: 0.2612 Accuracy: 0.9223\n",
      "Epoch: 200/200 Train Loss: 0.1662 Accuracy: 0.9470 Time: 11.96938  | Val Loss: 0.2670 Accuracy: 0.9200\n",
      "#Parameter: 7588450 Accuracy: 0.92\n"
     ]
    }
   ],
   "source": [
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    "    eval_image_classifier,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    net,\n",
    "    lr=1e-3,\n",
    "    weight_decay=0,\n",
    "    max_epochs=200,\n",
    ") -> dict[str, list[float]]:\n",
    "\n",
    "    print(f\"#Parameter: {count_trainable_parameter(net)}\")\n",
    "    criteria = nn.CrossEntropyLoss()\n",
    "    optimizer = torch.optim.AdamW(net.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "        optimizer, max_epochs, eta_min=lr / 100\n",
    "    )\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return training_stats\n",
    "\n",
    "\n",
    "train_dataloader, val_dataloader = build_dataloader(64, train_dataset, val_dataset)\n",
    "\n",
    "net = ShuffleNetV1(\n",
    "    num_classes=10,\n",
    "    group=8,\n",
    "    width_multiplier=1,\n",
    "    dropout=0,\n",
    ").to(DEVICE)\n",
    "width_multiplier_1 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    net,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c5fe4b6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 4293130\n",
      "Epoch: 1/200 Train Loss: 2.1145 Accuracy: 0.2523 Time: 8.33201  | Val Loss: 1.9252 Accuracy: 0.3511\n",
      "Epoch: 2/200 Train Loss: 1.8263 Accuracy: 0.3801 Time: 8.35828  | Val Loss: 1.4548 Accuracy: 0.5144\n",
      "Epoch: 3/200 Train Loss: 1.6380 Accuracy: 0.4440 Time: 8.33114  | Val Loss: 1.3983 Accuracy: 0.5508\n",
      "Epoch: 4/200 Train Loss: 1.5333 Accuracy: 0.4892 Time: 8.30735  | Val Loss: 1.3167 Accuracy: 0.5819\n",
      "Epoch: 5/200 Train Loss: 1.4559 Accuracy: 0.5134 Time: 8.33318  | Val Loss: 1.3632 Accuracy: 0.5595\n",
      "Epoch: 6/200 Train Loss: 1.4018 Accuracy: 0.5355 Time: 8.39578  | Val Loss: 1.0578 Accuracy: 0.6558\n",
      "Epoch: 7/200 Train Loss: 1.3543 Accuracy: 0.5520 Time: 8.34919  | Val Loss: 0.9298 Accuracy: 0.7001\n",
      "Epoch: 8/200 Train Loss: 1.2640 Accuracy: 0.5836 Time: 8.40338  | Val Loss: 1.0247 Accuracy: 0.6818\n",
      "Epoch: 9/200 Train Loss: 1.2254 Accuracy: 0.5953 Time: 8.46951  | Val Loss: 0.9249 Accuracy: 0.7032\n",
      "Epoch: 10/200 Train Loss: 1.1865 Accuracy: 0.6125 Time: 8.37430  | Val Loss: 0.7565 Accuracy: 0.7648\n",
      "Epoch: 11/200 Train Loss: 1.1569 Accuracy: 0.6214 Time: 8.35129  | Val Loss: 0.7896 Accuracy: 0.7488\n",
      "Epoch: 12/200 Train Loss: 1.1008 Accuracy: 0.6445 Time: 8.40469  | Val Loss: 0.8877 Accuracy: 0.7197\n",
      "Epoch: 13/200 Train Loss: 1.0903 Accuracy: 0.6446 Time: 8.43554  | Val Loss: 0.8355 Accuracy: 0.7376\n",
      "Epoch: 14/200 Train Loss: 1.0275 Accuracy: 0.6675 Time: 8.41162  | Val Loss: 0.6933 Accuracy: 0.7799\n",
      "Epoch: 15/200 Train Loss: 1.0185 Accuracy: 0.6680 Time: 8.34313  | Val Loss: 0.6929 Accuracy: 0.7743\n",
      "Epoch: 16/200 Train Loss: 0.9926 Accuracy: 0.6723 Time: 8.37838  | Val Loss: 0.7649 Accuracy: 0.7674\n",
      "Epoch: 17/200 Train Loss: 0.9930 Accuracy: 0.6777 Time: 8.39965  | Val Loss: 0.7059 Accuracy: 0.7735\n",
      "Epoch: 18/200 Train Loss: 0.9347 Accuracy: 0.6935 Time: 8.44638  | Val Loss: 0.6625 Accuracy: 0.7903\n",
      "Epoch: 19/200 Train Loss: 0.9490 Accuracy: 0.6926 Time: 8.41628  | Val Loss: 0.6388 Accuracy: 0.7957\n",
      "Epoch: 20/200 Train Loss: 0.9024 Accuracy: 0.7059 Time: 8.48732  | Val Loss: 0.6466 Accuracy: 0.8000\n",
      "Epoch: 21/200 Train Loss: 0.9059 Accuracy: 0.7052 Time: 8.41868  | Val Loss: 0.5758 Accuracy: 0.8227\n",
      "Epoch: 22/200 Train Loss: 0.8765 Accuracy: 0.7132 Time: 8.36332  | Val Loss: 0.6552 Accuracy: 0.7929\n",
      "Epoch: 23/200 Train Loss: 0.8527 Accuracy: 0.7275 Time: 8.37381  | Val Loss: 0.5749 Accuracy: 0.8201\n",
      "Epoch: 24/200 Train Loss: 0.8452 Accuracy: 0.7274 Time: 8.42659  | Val Loss: 0.6391 Accuracy: 0.7967\n",
      "Epoch: 25/200 Train Loss: 0.8396 Accuracy: 0.7296 Time: 8.30615  | Val Loss: 0.5392 Accuracy: 0.8280\n",
      "Epoch: 26/200 Train Loss: 0.7999 Accuracy: 0.7405 Time: 8.37105  | Val Loss: 0.5951 Accuracy: 0.8166\n",
      "Epoch: 27/200 Train Loss: 0.7962 Accuracy: 0.7420 Time: 8.37672  | Val Loss: 0.5559 Accuracy: 0.8209\n",
      "Epoch: 28/200 Train Loss: 0.7854 Accuracy: 0.7440 Time: 8.45813  | Val Loss: 0.5429 Accuracy: 0.8273\n",
      "Epoch: 29/200 Train Loss: 0.7716 Accuracy: 0.7479 Time: 8.38374  | Val Loss: 0.5403 Accuracy: 0.8308\n",
      "Epoch: 30/200 Train Loss: 0.7502 Accuracy: 0.7577 Time: 8.37347  | Val Loss: 0.5651 Accuracy: 0.8285\n",
      "Epoch: 31/200 Train Loss: 0.7373 Accuracy: 0.7583 Time: 8.37537  | Val Loss: 0.5676 Accuracy: 0.8201\n",
      "Epoch: 32/200 Train Loss: 0.7438 Accuracy: 0.7594 Time: 8.51079  | Val Loss: 0.5993 Accuracy: 0.8201\n",
      "Epoch: 33/200 Train Loss: 0.7120 Accuracy: 0.7725 Time: 8.40609  | Val Loss: 0.5569 Accuracy: 0.8262\n",
      "Epoch: 34/200 Train Loss: 0.6970 Accuracy: 0.7708 Time: 8.39366  | Val Loss: 0.4914 Accuracy: 0.8400\n",
      "Epoch: 35/200 Train Loss: 0.7219 Accuracy: 0.7645 Time: 8.36908  | Val Loss: 0.5188 Accuracy: 0.8413\n",
      "Epoch: 36/200 Train Loss: 0.6860 Accuracy: 0.7760 Time: 8.43503  | Val Loss: 0.5592 Accuracy: 0.8288\n",
      "Epoch: 37/200 Train Loss: 0.7064 Accuracy: 0.7686 Time: 8.40807  | Val Loss: 0.5073 Accuracy: 0.8395\n",
      "Epoch: 38/200 Train Loss: 0.6842 Accuracy: 0.7776 Time: 8.44395  | Val Loss: 0.4665 Accuracy: 0.8504\n",
      "Epoch: 39/200 Train Loss: 0.6761 Accuracy: 0.7777 Time: 8.42571  | Val Loss: 0.4912 Accuracy: 0.8408\n",
      "Epoch: 40/200 Train Loss: 0.6572 Accuracy: 0.7908 Time: 8.44703  | Val Loss: 0.4937 Accuracy: 0.8410\n",
      "Epoch: 41/200 Train Loss: 0.6566 Accuracy: 0.7877 Time: 8.41402  | Val Loss: 0.4749 Accuracy: 0.8555\n",
      "Epoch: 42/200 Train Loss: 0.6386 Accuracy: 0.7961 Time: 8.45673  | Val Loss: 0.4685 Accuracy: 0.8482\n",
      "Epoch: 43/200 Train Loss: 0.6222 Accuracy: 0.7942 Time: 8.36891  | Val Loss: 0.4429 Accuracy: 0.8611\n",
      "Epoch: 44/200 Train Loss: 0.6307 Accuracy: 0.7954 Time: 8.42161  | Val Loss: 0.4607 Accuracy: 0.8520\n",
      "Epoch: 45/200 Train Loss: 0.6225 Accuracy: 0.7950 Time: 8.36186  | Val Loss: 0.4492 Accuracy: 0.8639\n",
      "Epoch: 46/200 Train Loss: 0.6101 Accuracy: 0.7990 Time: 8.38024  | Val Loss: 0.4151 Accuracy: 0.8708\n",
      "Epoch: 47/200 Train Loss: 0.6166 Accuracy: 0.7981 Time: 8.41855  | Val Loss: 0.4593 Accuracy: 0.8601\n",
      "Epoch: 48/200 Train Loss: 0.6025 Accuracy: 0.8067 Time: 8.40829  | Val Loss: 0.4346 Accuracy: 0.8693\n",
      "Epoch: 49/200 Train Loss: 0.6132 Accuracy: 0.8001 Time: 8.37905  | Val Loss: 0.4061 Accuracy: 0.8746\n",
      "Epoch: 50/200 Train Loss: 0.5889 Accuracy: 0.8071 Time: 8.41388  | Val Loss: 0.4313 Accuracy: 0.8670\n",
      "Epoch: 51/200 Train Loss: 0.5877 Accuracy: 0.8064 Time: 8.38049  | Val Loss: 0.4472 Accuracy: 0.8637\n",
      "Epoch: 52/200 Train Loss: 0.5781 Accuracy: 0.8124 Time: 8.44821  | Val Loss: 0.4435 Accuracy: 0.8665\n",
      "Epoch: 53/200 Train Loss: 0.5843 Accuracy: 0.8072 Time: 8.45059  | Val Loss: 0.3966 Accuracy: 0.8749\n",
      "Epoch: 54/200 Train Loss: 0.5758 Accuracy: 0.8120 Time: 8.41939  | Val Loss: 0.4304 Accuracy: 0.8665\n",
      "Epoch: 55/200 Train Loss: 0.5523 Accuracy: 0.8193 Time: 8.35333  | Val Loss: 0.4289 Accuracy: 0.8637\n",
      "Epoch: 56/200 Train Loss: 0.5459 Accuracy: 0.8213 Time: 8.42106  | Val Loss: 0.4015 Accuracy: 0.8780\n",
      "Epoch: 57/200 Train Loss: 0.5559 Accuracy: 0.8170 Time: 8.39372  | Val Loss: 0.4094 Accuracy: 0.8696\n",
      "Epoch: 58/200 Train Loss: 0.5476 Accuracy: 0.8215 Time: 8.47517  | Val Loss: 0.4202 Accuracy: 0.8741\n",
      "Epoch: 59/200 Train Loss: 0.5394 Accuracy: 0.8205 Time: 8.45099  | Val Loss: 0.4035 Accuracy: 0.8721\n",
      "Epoch: 60/200 Train Loss: 0.5098 Accuracy: 0.8320 Time: 8.42323  | Val Loss: 0.4154 Accuracy: 0.8780\n",
      "Epoch: 61/200 Train Loss: 0.5297 Accuracy: 0.8262 Time: 8.37344  | Val Loss: 0.3882 Accuracy: 0.8805\n",
      "Epoch: 62/200 Train Loss: 0.5401 Accuracy: 0.8261 Time: 8.36891  | Val Loss: 0.3975 Accuracy: 0.8759\n",
      "Epoch: 63/200 Train Loss: 0.5065 Accuracy: 0.8335 Time: 8.44104  | Val Loss: 0.4004 Accuracy: 0.8746\n",
      "Epoch: 64/200 Train Loss: 0.5061 Accuracy: 0.8340 Time: 8.40149  | Val Loss: 0.3790 Accuracy: 0.8874\n",
      "Epoch: 65/200 Train Loss: 0.5052 Accuracy: 0.8358 Time: 8.44768  | Val Loss: 0.4119 Accuracy: 0.8718\n",
      "Epoch: 66/200 Train Loss: 0.5005 Accuracy: 0.8348 Time: 8.40354  | Val Loss: 0.3685 Accuracy: 0.8874\n",
      "Epoch: 67/200 Train Loss: 0.4942 Accuracy: 0.8400 Time: 8.40140  | Val Loss: 0.3811 Accuracy: 0.8836\n",
      "Epoch: 68/200 Train Loss: 0.4868 Accuracy: 0.8400 Time: 8.37114  | Val Loss: 0.3739 Accuracy: 0.8831\n",
      "Epoch: 69/200 Train Loss: 0.4659 Accuracy: 0.8455 Time: 8.54894  | Val Loss: 0.3759 Accuracy: 0.8846\n",
      "Epoch: 70/200 Train Loss: 0.4584 Accuracy: 0.8474 Time: 8.34574  | Val Loss: 0.3617 Accuracy: 0.8846\n",
      "Epoch: 71/200 Train Loss: 0.4790 Accuracy: 0.8416 Time: 8.45756  | Val Loss: 0.3846 Accuracy: 0.8805\n",
      "Epoch: 72/200 Train Loss: 0.4727 Accuracy: 0.8466 Time: 8.42369  | Val Loss: 0.3598 Accuracy: 0.8899\n",
      "Epoch: 73/200 Train Loss: 0.4743 Accuracy: 0.8432 Time: 8.41113  | Val Loss: 0.3703 Accuracy: 0.8856\n",
      "Epoch: 74/200 Train Loss: 0.4603 Accuracy: 0.8486 Time: 8.38305  | Val Loss: 0.3677 Accuracy: 0.8892\n",
      "Epoch: 75/200 Train Loss: 0.4753 Accuracy: 0.8448 Time: 8.39132  | Val Loss: 0.3618 Accuracy: 0.8884\n",
      "Epoch: 76/200 Train Loss: 0.4423 Accuracy: 0.8546 Time: 8.49709  | Val Loss: 0.3722 Accuracy: 0.8869\n",
      "Epoch: 77/200 Train Loss: 0.4424 Accuracy: 0.8510 Time: 8.36302  | Val Loss: 0.3602 Accuracy: 0.8907\n",
      "Epoch: 78/200 Train Loss: 0.4452 Accuracy: 0.8544 Time: 8.30598  | Val Loss: 0.3718 Accuracy: 0.8889\n",
      "Epoch: 79/200 Train Loss: 0.4477 Accuracy: 0.8528 Time: 8.45940  | Val Loss: 0.3424 Accuracy: 0.8948\n",
      "Epoch: 80/200 Train Loss: 0.4439 Accuracy: 0.8520 Time: 8.46642  | Val Loss: 0.3615 Accuracy: 0.8948\n",
      "Epoch: 81/200 Train Loss: 0.4330 Accuracy: 0.8586 Time: 8.40567  | Val Loss: 0.3470 Accuracy: 0.8932\n",
      "Epoch: 82/200 Train Loss: 0.4401 Accuracy: 0.8573 Time: 8.41814  | Val Loss: 0.3417 Accuracy: 0.8973\n",
      "Epoch: 83/200 Train Loss: 0.4210 Accuracy: 0.8620 Time: 8.40905  | Val Loss: 0.3275 Accuracy: 0.8991\n",
      "Epoch: 84/200 Train Loss: 0.4228 Accuracy: 0.8615 Time: 8.41236  | Val Loss: 0.3452 Accuracy: 0.8971\n",
      "Epoch: 85/200 Train Loss: 0.4208 Accuracy: 0.8602 Time: 8.42579  | Val Loss: 0.3691 Accuracy: 0.8907\n",
      "Epoch: 86/200 Train Loss: 0.4065 Accuracy: 0.8678 Time: 8.49732  | Val Loss: 0.3514 Accuracy: 0.8935\n",
      "Epoch: 87/200 Train Loss: 0.4177 Accuracy: 0.8638 Time: 8.36841  | Val Loss: 0.3920 Accuracy: 0.8879\n",
      "Epoch: 88/200 Train Loss: 0.4136 Accuracy: 0.8619 Time: 8.42107  | Val Loss: 0.3493 Accuracy: 0.8978\n",
      "Epoch: 89/200 Train Loss: 0.4034 Accuracy: 0.8668 Time: 8.46172  | Val Loss: 0.3705 Accuracy: 0.8927\n",
      "Epoch: 90/200 Train Loss: 0.3897 Accuracy: 0.8717 Time: 8.41867  | Val Loss: 0.3587 Accuracy: 0.8943\n",
      "Epoch: 91/200 Train Loss: 0.3998 Accuracy: 0.8706 Time: 8.40202  | Val Loss: 0.3460 Accuracy: 0.9019\n",
      "Epoch: 92/200 Train Loss: 0.3946 Accuracy: 0.8713 Time: 8.42478  | Val Loss: 0.3531 Accuracy: 0.8927\n",
      "Epoch: 93/200 Train Loss: 0.3886 Accuracy: 0.8694 Time: 8.43234  | Val Loss: 0.3669 Accuracy: 0.8925\n",
      "Epoch: 94/200 Train Loss: 0.3859 Accuracy: 0.8699 Time: 8.33722  | Val Loss: 0.3424 Accuracy: 0.8966\n",
      "Epoch: 95/200 Train Loss: 0.3796 Accuracy: 0.8744 Time: 8.44139  | Val Loss: 0.3392 Accuracy: 0.8978\n",
      "Epoch: 96/200 Train Loss: 0.3599 Accuracy: 0.8830 Time: 8.43452  | Val Loss: 0.3358 Accuracy: 0.9014\n",
      "Epoch: 97/200 Train Loss: 0.3806 Accuracy: 0.8771 Time: 8.50435  | Val Loss: 0.3401 Accuracy: 0.8973\n",
      "Epoch: 98/200 Train Loss: 0.3688 Accuracy: 0.8786 Time: 8.35162  | Val Loss: 0.3171 Accuracy: 0.9060\n",
      "Epoch: 99/200 Train Loss: 0.3669 Accuracy: 0.8760 Time: 8.50742  | Val Loss: 0.3091 Accuracy: 0.9039\n",
      "Epoch: 100/200 Train Loss: 0.3675 Accuracy: 0.8792 Time: 8.44724  | Val Loss: 0.3435 Accuracy: 0.8999\n",
      "Epoch: 101/200 Train Loss: 0.3472 Accuracy: 0.8859 Time: 8.44304  | Val Loss: 0.3279 Accuracy: 0.9022\n",
      "Epoch: 102/200 Train Loss: 0.3747 Accuracy: 0.8792 Time: 8.34530  | Val Loss: 0.3081 Accuracy: 0.9083\n",
      "Epoch: 103/200 Train Loss: 0.3581 Accuracy: 0.8818 Time: 8.37917  | Val Loss: 0.3170 Accuracy: 0.9045\n",
      "Epoch: 104/200 Train Loss: 0.3550 Accuracy: 0.8872 Time: 8.35279  | Val Loss: 0.3170 Accuracy: 0.9078\n",
      "Epoch: 105/200 Train Loss: 0.3491 Accuracy: 0.8859 Time: 8.32733  | Val Loss: 0.3461 Accuracy: 0.8991\n",
      "Epoch: 106/200 Train Loss: 0.3439 Accuracy: 0.8851 Time: 8.32846  | Val Loss: 0.3376 Accuracy: 0.9039\n",
      "Epoch: 107/200 Train Loss: 0.3326 Accuracy: 0.8905 Time: 8.34446  | Val Loss: 0.3330 Accuracy: 0.9001\n",
      "Epoch: 108/200 Train Loss: 0.3404 Accuracy: 0.8874 Time: 8.41523  | Val Loss: 0.3221 Accuracy: 0.9116\n",
      "Epoch: 109/200 Train Loss: 0.3265 Accuracy: 0.8906 Time: 8.36615  | Val Loss: 0.3105 Accuracy: 0.9088\n",
      "Epoch: 110/200 Train Loss: 0.3300 Accuracy: 0.8893 Time: 8.45164  | Val Loss: 0.3055 Accuracy: 0.9098\n",
      "Epoch: 111/200 Train Loss: 0.3256 Accuracy: 0.8901 Time: 8.47364  | Val Loss: 0.3286 Accuracy: 0.9080\n",
      "Epoch: 112/200 Train Loss: 0.3249 Accuracy: 0.8930 Time: 8.41853  | Val Loss: 0.3169 Accuracy: 0.9111\n",
      "Epoch: 113/200 Train Loss: 0.3205 Accuracy: 0.8947 Time: 8.49988  | Val Loss: 0.3371 Accuracy: 0.8989\n",
      "Epoch: 114/200 Train Loss: 0.3307 Accuracy: 0.8894 Time: 8.40904  | Val Loss: 0.3199 Accuracy: 0.9052\n",
      "Epoch: 115/200 Train Loss: 0.3185 Accuracy: 0.8957 Time: 8.38100  | Val Loss: 0.3215 Accuracy: 0.9085\n",
      "Epoch: 116/200 Train Loss: 0.3050 Accuracy: 0.8996 Time: 8.38477  | Val Loss: 0.3084 Accuracy: 0.9083\n",
      "Epoch: 117/200 Train Loss: 0.3040 Accuracy: 0.9002 Time: 8.41424  | Val Loss: 0.3030 Accuracy: 0.9060\n",
      "Epoch: 118/200 Train Loss: 0.3139 Accuracy: 0.8963 Time: 8.47499  | Val Loss: 0.3158 Accuracy: 0.9047\n",
      "Epoch: 119/200 Train Loss: 0.3002 Accuracy: 0.9029 Time: 8.40333  | Val Loss: 0.3098 Accuracy: 0.9052\n",
      "Epoch: 120/200 Train Loss: 0.3119 Accuracy: 0.8963 Time: 8.38356  | Val Loss: 0.3367 Accuracy: 0.9011\n",
      "Epoch: 121/200 Train Loss: 0.2963 Accuracy: 0.9025 Time: 8.37412  | Val Loss: 0.3146 Accuracy: 0.9052\n",
      "Epoch: 122/200 Train Loss: 0.2922 Accuracy: 0.9057 Time: 8.41434  | Val Loss: 0.3188 Accuracy: 0.9083\n",
      "Epoch: 123/200 Train Loss: 0.3100 Accuracy: 0.8945 Time: 8.39678  | Val Loss: 0.3140 Accuracy: 0.9075\n",
      "Epoch: 124/200 Train Loss: 0.2824 Accuracy: 0.9074 Time: 8.41046  | Val Loss: 0.3201 Accuracy: 0.9070\n",
      "Epoch: 125/200 Train Loss: 0.2907 Accuracy: 0.9027 Time: 8.37633  | Val Loss: 0.3033 Accuracy: 0.9101\n",
      "Epoch: 126/200 Train Loss: 0.2920 Accuracy: 0.9063 Time: 8.37947  | Val Loss: 0.3074 Accuracy: 0.9101\n",
      "Epoch: 127/200 Train Loss: 0.2825 Accuracy: 0.9045 Time: 8.38064  | Val Loss: 0.3124 Accuracy: 0.9111\n",
      "Epoch: 128/200 Train Loss: 0.2763 Accuracy: 0.9085 Time: 8.40227  | Val Loss: 0.3170 Accuracy: 0.9062\n",
      "Epoch: 129/200 Train Loss: 0.2810 Accuracy: 0.9079 Time: 8.47441  | Val Loss: 0.3160 Accuracy: 0.9096\n",
      "Epoch: 130/200 Train Loss: 0.2840 Accuracy: 0.9038 Time: 8.37527  | Val Loss: 0.3227 Accuracy: 0.9075\n",
      "Epoch: 131/200 Train Loss: 0.2763 Accuracy: 0.9103 Time: 8.37728  | Val Loss: 0.3032 Accuracy: 0.9093\n",
      "Epoch: 132/200 Train Loss: 0.2756 Accuracy: 0.9083 Time: 8.44528  | Val Loss: 0.3234 Accuracy: 0.9050\n",
      "Epoch: 133/200 Train Loss: 0.2842 Accuracy: 0.9034 Time: 8.40321  | Val Loss: 0.3116 Accuracy: 0.9090\n",
      "Epoch: 134/200 Train Loss: 0.2787 Accuracy: 0.9089 Time: 8.39487  | Val Loss: 0.3070 Accuracy: 0.9126\n",
      "Epoch: 135/200 Train Loss: 0.2751 Accuracy: 0.9107 Time: 8.45441  | Val Loss: 0.3220 Accuracy: 0.9078\n",
      "Epoch: 136/200 Train Loss: 0.2619 Accuracy: 0.9134 Time: 8.46121  | Val Loss: 0.3059 Accuracy: 0.9131\n",
      "Epoch: 137/200 Train Loss: 0.2805 Accuracy: 0.9091 Time: 8.43648  | Val Loss: 0.3088 Accuracy: 0.9116\n",
      "Epoch: 138/200 Train Loss: 0.2682 Accuracy: 0.9134 Time: 8.38032  | Val Loss: 0.3100 Accuracy: 0.9068\n",
      "Epoch: 139/200 Train Loss: 0.2553 Accuracy: 0.9147 Time: 8.36697  | Val Loss: 0.3057 Accuracy: 0.9129\n",
      "Epoch: 140/200 Train Loss: 0.2656 Accuracy: 0.9149 Time: 8.43914  | Val Loss: 0.3090 Accuracy: 0.9108\n",
      "Epoch: 141/200 Train Loss: 0.2540 Accuracy: 0.9158 Time: 8.33114  | Val Loss: 0.3063 Accuracy: 0.9101\n",
      "Epoch: 142/200 Train Loss: 0.2515 Accuracy: 0.9160 Time: 8.34623  | Val Loss: 0.3052 Accuracy: 0.9121\n",
      "Epoch: 143/200 Train Loss: 0.2579 Accuracy: 0.9140 Time: 8.44310  | Val Loss: 0.3082 Accuracy: 0.9075\n",
      "Epoch: 144/200 Train Loss: 0.2589 Accuracy: 0.9165 Time: 8.47703  | Val Loss: 0.3098 Accuracy: 0.9088\n",
      "Epoch: 145/200 Train Loss: 0.2488 Accuracy: 0.9187 Time: 8.42051  | Val Loss: 0.3090 Accuracy: 0.9083\n",
      "Epoch: 146/200 Train Loss: 0.2448 Accuracy: 0.9223 Time: 8.36257  | Val Loss: 0.3023 Accuracy: 0.9136\n",
      "Epoch: 147/200 Train Loss: 0.2371 Accuracy: 0.9191 Time: 8.35641  | Val Loss: 0.3059 Accuracy: 0.9129\n",
      "Epoch: 148/200 Train Loss: 0.2465 Accuracy: 0.9193 Time: 8.35396  | Val Loss: 0.3003 Accuracy: 0.9134\n",
      "Epoch: 149/200 Train Loss: 0.2359 Accuracy: 0.9220 Time: 8.34851  | Val Loss: 0.3096 Accuracy: 0.9090\n",
      "Epoch: 150/200 Train Loss: 0.2452 Accuracy: 0.9203 Time: 8.39980  | Val Loss: 0.3029 Accuracy: 0.9103\n",
      "Epoch: 151/200 Train Loss: 0.2511 Accuracy: 0.9157 Time: 8.45400  | Val Loss: 0.2984 Accuracy: 0.9118\n",
      "Epoch: 152/200 Train Loss: 0.2407 Accuracy: 0.9216 Time: 8.37331  | Val Loss: 0.2986 Accuracy: 0.9144\n",
      "Epoch: 153/200 Train Loss: 0.2355 Accuracy: 0.9238 Time: 8.42908  | Val Loss: 0.3037 Accuracy: 0.9129\n",
      "Epoch: 154/200 Train Loss: 0.2305 Accuracy: 0.9236 Time: 8.42157  | Val Loss: 0.3000 Accuracy: 0.9134\n",
      "Epoch: 155/200 Train Loss: 0.2282 Accuracy: 0.9257 Time: 8.34071  | Val Loss: 0.2981 Accuracy: 0.9154\n",
      "Epoch: 156/200 Train Loss: 0.2336 Accuracy: 0.9234 Time: 8.34248  | Val Loss: 0.3056 Accuracy: 0.9169\n",
      "Epoch: 157/200 Train Loss: 0.2293 Accuracy: 0.9230 Time: 8.39773  | Val Loss: 0.3080 Accuracy: 0.9134\n",
      "Epoch: 158/200 Train Loss: 0.2239 Accuracy: 0.9279 Time: 8.37368  | Val Loss: 0.3048 Accuracy: 0.9136\n",
      "Epoch: 159/200 Train Loss: 0.2211 Accuracy: 0.9267 Time: 8.40784  | Val Loss: 0.2980 Accuracy: 0.9136\n",
      "Epoch: 160/200 Train Loss: 0.2285 Accuracy: 0.9257 Time: 8.42813  | Val Loss: 0.2954 Accuracy: 0.9162\n",
      "Epoch: 161/200 Train Loss: 0.2121 Accuracy: 0.9298 Time: 8.42828  | Val Loss: 0.3019 Accuracy: 0.9124\n",
      "Epoch: 162/200 Train Loss: 0.2327 Accuracy: 0.9245 Time: 8.35999  | Val Loss: 0.3110 Accuracy: 0.9085\n",
      "Epoch: 163/200 Train Loss: 0.2223 Accuracy: 0.9283 Time: 8.36907  | Val Loss: 0.2931 Accuracy: 0.9129\n",
      "Epoch: 164/200 Train Loss: 0.2188 Accuracy: 0.9293 Time: 8.38784  | Val Loss: 0.2997 Accuracy: 0.9139\n",
      "Epoch: 165/200 Train Loss: 0.2279 Accuracy: 0.9243 Time: 8.40392  | Val Loss: 0.2997 Accuracy: 0.9118\n",
      "Epoch: 166/200 Train Loss: 0.2167 Accuracy: 0.9271 Time: 8.45679  | Val Loss: 0.2924 Accuracy: 0.9136\n",
      "Epoch: 167/200 Train Loss: 0.2353 Accuracy: 0.9204 Time: 8.39468  | Val Loss: 0.2950 Accuracy: 0.9169\n",
      "Epoch: 168/200 Train Loss: 0.2258 Accuracy: 0.9271 Time: 8.43286  | Val Loss: 0.2949 Accuracy: 0.9157\n",
      "Epoch: 169/200 Train Loss: 0.2227 Accuracy: 0.9264 Time: 8.37013  | Val Loss: 0.2943 Accuracy: 0.9139\n",
      "Epoch: 170/200 Train Loss: 0.2162 Accuracy: 0.9288 Time: 8.39488  | Val Loss: 0.2935 Accuracy: 0.9164\n",
      "Epoch: 171/200 Train Loss: 0.2326 Accuracy: 0.9259 Time: 8.37625  | Val Loss: 0.2862 Accuracy: 0.9172\n",
      "Epoch: 172/200 Train Loss: 0.2260 Accuracy: 0.9276 Time: 8.35826  | Val Loss: 0.2895 Accuracy: 0.9182\n",
      "Epoch: 173/200 Train Loss: 0.2216 Accuracy: 0.9282 Time: 8.42830  | Val Loss: 0.2890 Accuracy: 0.9197\n",
      "Epoch: 174/200 Train Loss: 0.2164 Accuracy: 0.9303 Time: 8.34135  | Val Loss: 0.2886 Accuracy: 0.9167\n",
      "Epoch: 175/200 Train Loss: 0.2209 Accuracy: 0.9272 Time: 8.49305  | Val Loss: 0.2948 Accuracy: 0.9159\n",
      "Epoch: 176/200 Train Loss: 0.2070 Accuracy: 0.9339 Time: 8.37829  | Val Loss: 0.2920 Accuracy: 0.9177\n",
      "Epoch: 177/200 Train Loss: 0.2146 Accuracy: 0.9281 Time: 8.37342  | Val Loss: 0.2936 Accuracy: 0.9154\n",
      "Epoch: 178/200 Train Loss: 0.2035 Accuracy: 0.9336 Time: 8.40678  | Val Loss: 0.2885 Accuracy: 0.9159\n",
      "Epoch: 179/200 Train Loss: 0.2082 Accuracy: 0.9312 Time: 8.40289  | Val Loss: 0.2864 Accuracy: 0.9149\n",
      "Epoch: 180/200 Train Loss: 0.1971 Accuracy: 0.9328 Time: 8.40805  | Val Loss: 0.2869 Accuracy: 0.9182\n",
      "Epoch: 181/200 Train Loss: 0.2097 Accuracy: 0.9325 Time: 8.44023  | Val Loss: 0.2854 Accuracy: 0.9185\n",
      "Epoch: 182/200 Train Loss: 0.2108 Accuracy: 0.9321 Time: 8.34855  | Val Loss: 0.2829 Accuracy: 0.9213\n",
      "Epoch: 183/200 Train Loss: 0.2077 Accuracy: 0.9354 Time: 8.42403  | Val Loss: 0.2910 Accuracy: 0.9172\n",
      "Epoch: 184/200 Train Loss: 0.2194 Accuracy: 0.9295 Time: 8.43060  | Val Loss: 0.2843 Accuracy: 0.9190\n",
      "Epoch: 185/200 Train Loss: 0.2036 Accuracy: 0.9335 Time: 8.35961  | Val Loss: 0.2825 Accuracy: 0.9223\n",
      "Epoch: 186/200 Train Loss: 0.2143 Accuracy: 0.9307 Time: 8.38297  | Val Loss: 0.2910 Accuracy: 0.9167\n",
      "Epoch: 187/200 Train Loss: 0.2121 Accuracy: 0.9317 Time: 8.37900  | Val Loss: 0.2926 Accuracy: 0.9172\n",
      "Epoch: 188/200 Train Loss: 0.2134 Accuracy: 0.9317 Time: 8.44504  | Val Loss: 0.2877 Accuracy: 0.9180\n",
      "Epoch: 189/200 Train Loss: 0.2066 Accuracy: 0.9336 Time: 8.37808  | Val Loss: 0.2825 Accuracy: 0.9177\n",
      "Epoch: 190/200 Train Loss: 0.2071 Accuracy: 0.9319 Time: 8.35090  | Val Loss: 0.2891 Accuracy: 0.9177\n",
      "Epoch: 191/200 Train Loss: 0.2081 Accuracy: 0.9326 Time: 8.43789  | Val Loss: 0.2876 Accuracy: 0.9180\n",
      "Epoch: 192/200 Train Loss: 0.2089 Accuracy: 0.9314 Time: 8.37804  | Val Loss: 0.2854 Accuracy: 0.9175\n",
      "Epoch: 193/200 Train Loss: 0.2094 Accuracy: 0.9318 Time: 8.39927  | Val Loss: 0.2856 Accuracy: 0.9175\n",
      "Epoch: 194/200 Train Loss: 0.2093 Accuracy: 0.9295 Time: 8.41882  | Val Loss: 0.2891 Accuracy: 0.9157\n",
      "Epoch: 195/200 Train Loss: 0.2094 Accuracy: 0.9306 Time: 8.38400  | Val Loss: 0.2835 Accuracy: 0.9172\n",
      "Epoch: 196/200 Train Loss: 0.2070 Accuracy: 0.9301 Time: 8.42342  | Val Loss: 0.2860 Accuracy: 0.9175\n",
      "Epoch: 197/200 Train Loss: 0.2152 Accuracy: 0.9323 Time: 8.39805  | Val Loss: 0.2857 Accuracy: 0.9159\n",
      "Epoch: 198/200 Train Loss: 0.2085 Accuracy: 0.9330 Time: 8.46735  | Val Loss: 0.2846 Accuracy: 0.9164\n",
      "Epoch: 199/200 Train Loss: 0.2004 Accuracy: 0.9362 Time: 8.35852  | Val Loss: 0.2836 Accuracy: 0.9180\n",
      "Epoch: 200/200 Train Loss: 0.2065 Accuracy: 0.9327 Time: 8.36704  | Val Loss: 0.2825 Accuracy: 0.9172\n",
      "#Parameter: 4293130 Accuracy: 0.9171974522292994\n"
     ]
    }
   ],
   "source": [
    "net = ShuffleNetV1(\n",
    "    num_classes=10,\n",
    "    group=8,\n",
    "    width_multiplier=0.75,\n",
    "    dropout=0,\n",
    ").to(DEVICE)\n",
    "width_multiplier_75 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    net,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "74aa35d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 1930066\n",
      "Epoch: 1/200 Train Loss: 2.1060 Accuracy: 0.2468 Time: 6.72420  | Val Loss: 1.8938 Accuracy: 0.3381\n",
      "Epoch: 2/200 Train Loss: 1.8103 Accuracy: 0.3798 Time: 6.76190  | Val Loss: 1.5079 Accuracy: 0.4910\n",
      "Epoch: 3/200 Train Loss: 1.6800 Accuracy: 0.4349 Time: 6.70241  | Val Loss: 1.3193 Accuracy: 0.5529\n",
      "Epoch: 4/200 Train Loss: 1.5755 Accuracy: 0.4657 Time: 6.70783  | Val Loss: 1.3660 Accuracy: 0.5567\n",
      "Epoch: 5/200 Train Loss: 1.4889 Accuracy: 0.5087 Time: 6.70230  | Val Loss: 1.2464 Accuracy: 0.5776\n",
      "Epoch: 6/200 Train Loss: 1.4356 Accuracy: 0.5276 Time: 6.70042  | Val Loss: 1.1289 Accuracy: 0.6339\n",
      "Epoch: 7/200 Train Loss: 1.3768 Accuracy: 0.5447 Time: 6.75012  | Val Loss: 1.2432 Accuracy: 0.5985\n",
      "Epoch: 8/200 Train Loss: 1.3111 Accuracy: 0.5605 Time: 6.80263  | Val Loss: 0.9809 Accuracy: 0.6813\n",
      "Epoch: 9/200 Train Loss: 1.2651 Accuracy: 0.5769 Time: 6.79302  | Val Loss: 0.9494 Accuracy: 0.6922\n",
      "Epoch: 10/200 Train Loss: 1.2388 Accuracy: 0.5933 Time: 6.87276  | Val Loss: 1.0777 Accuracy: 0.6492\n",
      "Epoch: 11/200 Train Loss: 1.2139 Accuracy: 0.6012 Time: 6.65082  | Val Loss: 0.9588 Accuracy: 0.6864\n",
      "Epoch: 12/200 Train Loss: 1.1727 Accuracy: 0.6082 Time: 6.66826  | Val Loss: 0.8681 Accuracy: 0.7182\n",
      "Epoch: 13/200 Train Loss: 1.1530 Accuracy: 0.6172 Time: 6.65667  | Val Loss: 0.8171 Accuracy: 0.7348\n",
      "Epoch: 14/200 Train Loss: 1.1164 Accuracy: 0.6354 Time: 6.68498  | Val Loss: 0.8584 Accuracy: 0.7231\n",
      "Epoch: 15/200 Train Loss: 1.0938 Accuracy: 0.6381 Time: 6.71675  | Val Loss: 0.8348 Accuracy: 0.7396\n",
      "Epoch: 16/200 Train Loss: 1.0739 Accuracy: 0.6464 Time: 6.71975  | Val Loss: 0.7826 Accuracy: 0.7516\n",
      "Epoch: 17/200 Train Loss: 1.0590 Accuracy: 0.6573 Time: 6.73723  | Val Loss: 0.6931 Accuracy: 0.7791\n",
      "Epoch: 18/200 Train Loss: 1.0259 Accuracy: 0.6652 Time: 6.73130  | Val Loss: 0.7947 Accuracy: 0.7567\n",
      "Epoch: 19/200 Train Loss: 1.0226 Accuracy: 0.6605 Time: 6.69719  | Val Loss: 0.7348 Accuracy: 0.7648\n",
      "Epoch: 20/200 Train Loss: 0.9906 Accuracy: 0.6783 Time: 6.71777  | Val Loss: 0.6359 Accuracy: 0.8000\n",
      "Epoch: 21/200 Train Loss: 0.9657 Accuracy: 0.6813 Time: 6.68995  | Val Loss: 0.6202 Accuracy: 0.7997\n",
      "Epoch: 22/200 Train Loss: 0.9589 Accuracy: 0.6898 Time: 6.71797  | Val Loss: 0.6807 Accuracy: 0.7791\n",
      "Epoch: 23/200 Train Loss: 0.9362 Accuracy: 0.6954 Time: 6.69590  | Val Loss: 0.6718 Accuracy: 0.7837\n",
      "Epoch: 24/200 Train Loss: 0.9274 Accuracy: 0.6985 Time: 6.67182  | Val Loss: 0.6130 Accuracy: 0.8076\n",
      "Epoch: 25/200 Train Loss: 0.9153 Accuracy: 0.7016 Time: 6.85224  | Val Loss: 0.6304 Accuracy: 0.7962\n",
      "Epoch: 26/200 Train Loss: 0.9110 Accuracy: 0.7025 Time: 6.73932  | Val Loss: 0.7455 Accuracy: 0.7590\n",
      "Epoch: 27/200 Train Loss: 0.8864 Accuracy: 0.7118 Time: 6.72724  | Val Loss: 0.5974 Accuracy: 0.8117\n",
      "Epoch: 28/200 Train Loss: 0.8538 Accuracy: 0.7214 Time: 6.71511  | Val Loss: 0.7882 Accuracy: 0.7564\n",
      "Epoch: 29/200 Train Loss: 0.8668 Accuracy: 0.7183 Time: 6.68784  | Val Loss: 0.5980 Accuracy: 0.8140\n",
      "Epoch: 30/200 Train Loss: 0.8316 Accuracy: 0.7256 Time: 6.79715  | Val Loss: 0.7117 Accuracy: 0.7829\n",
      "Epoch: 31/200 Train Loss: 0.8182 Accuracy: 0.7340 Time: 6.79364  | Val Loss: 0.5984 Accuracy: 0.8115\n",
      "Epoch: 32/200 Train Loss: 0.8243 Accuracy: 0.7303 Time: 6.82563  | Val Loss: 0.5243 Accuracy: 0.8372\n",
      "Epoch: 33/200 Train Loss: 0.8134 Accuracy: 0.7324 Time: 6.79405  | Val Loss: 0.5862 Accuracy: 0.8148\n",
      "Epoch: 34/200 Train Loss: 0.8045 Accuracy: 0.7386 Time: 6.76484  | Val Loss: 0.5631 Accuracy: 0.8283\n",
      "Epoch: 35/200 Train Loss: 0.7785 Accuracy: 0.7457 Time: 6.78590  | Val Loss: 0.5904 Accuracy: 0.8176\n",
      "Epoch: 36/200 Train Loss: 0.7789 Accuracy: 0.7449 Time: 6.74559  | Val Loss: 0.5244 Accuracy: 0.8385\n",
      "Epoch: 37/200 Train Loss: 0.7820 Accuracy: 0.7477 Time: 6.77708  | Val Loss: 0.5191 Accuracy: 0.8324\n",
      "Epoch: 38/200 Train Loss: 0.7710 Accuracy: 0.7507 Time: 6.72134  | Val Loss: 0.5186 Accuracy: 0.8326\n",
      "Epoch: 39/200 Train Loss: 0.7604 Accuracy: 0.7529 Time: 6.72425  | Val Loss: 0.5409 Accuracy: 0.8237\n",
      "Epoch: 40/200 Train Loss: 0.7320 Accuracy: 0.7634 Time: 6.74981  | Val Loss: 0.5606 Accuracy: 0.8155\n",
      "Epoch: 41/200 Train Loss: 0.7445 Accuracy: 0.7564 Time: 6.71192  | Val Loss: 0.5753 Accuracy: 0.8173\n",
      "Epoch: 42/200 Train Loss: 0.7316 Accuracy: 0.7677 Time: 6.78569  | Val Loss: 0.5269 Accuracy: 0.8293\n",
      "Epoch: 43/200 Train Loss: 0.7469 Accuracy: 0.7556 Time: 6.73392  | Val Loss: 0.4961 Accuracy: 0.8408\n",
      "Epoch: 44/200 Train Loss: 0.7016 Accuracy: 0.7692 Time: 6.71711  | Val Loss: 0.4845 Accuracy: 0.8448\n",
      "Epoch: 45/200 Train Loss: 0.7138 Accuracy: 0.7643 Time: 6.68593  | Val Loss: 0.5430 Accuracy: 0.8275\n",
      "Epoch: 46/200 Train Loss: 0.7017 Accuracy: 0.7711 Time: 6.74460  | Val Loss: 0.4962 Accuracy: 0.8357\n",
      "Epoch: 47/200 Train Loss: 0.6803 Accuracy: 0.7767 Time: 6.74619  | Val Loss: 0.4987 Accuracy: 0.8395\n",
      "Epoch: 48/200 Train Loss: 0.6696 Accuracy: 0.7816 Time: 6.79265  | Val Loss: 0.4798 Accuracy: 0.8499\n",
      "Epoch: 49/200 Train Loss: 0.6721 Accuracy: 0.7788 Time: 6.71285  | Val Loss: 0.4993 Accuracy: 0.8408\n",
      "Epoch: 50/200 Train Loss: 0.6610 Accuracy: 0.7871 Time: 6.76335  | Val Loss: 0.4770 Accuracy: 0.8418\n",
      "Epoch: 51/200 Train Loss: 0.6605 Accuracy: 0.7852 Time: 6.69193  | Val Loss: 0.4672 Accuracy: 0.8530\n",
      "Epoch: 52/200 Train Loss: 0.6535 Accuracy: 0.7853 Time: 6.68195  | Val Loss: 0.4587 Accuracy: 0.8571\n",
      "Epoch: 53/200 Train Loss: 0.6465 Accuracy: 0.7838 Time: 6.70083  | Val Loss: 0.4557 Accuracy: 0.8555\n",
      "Epoch: 54/200 Train Loss: 0.6266 Accuracy: 0.7910 Time: 6.76803  | Val Loss: 0.4551 Accuracy: 0.8553\n",
      "Epoch: 55/200 Train Loss: 0.6395 Accuracy: 0.7868 Time: 6.82771  | Val Loss: 0.4601 Accuracy: 0.8553\n",
      "Epoch: 56/200 Train Loss: 0.6465 Accuracy: 0.7843 Time: 6.81220  | Val Loss: 0.4332 Accuracy: 0.8668\n",
      "Epoch: 57/200 Train Loss: 0.6143 Accuracy: 0.7985 Time: 6.70643  | Val Loss: 0.4281 Accuracy: 0.8617\n",
      "Epoch: 58/200 Train Loss: 0.6379 Accuracy: 0.7936 Time: 6.70547  | Val Loss: 0.4593 Accuracy: 0.8538\n",
      "Epoch: 59/200 Train Loss: 0.6092 Accuracy: 0.7981 Time: 6.74395  | Val Loss: 0.4128 Accuracy: 0.8739\n",
      "Epoch: 60/200 Train Loss: 0.6067 Accuracy: 0.8018 Time: 6.84325  | Val Loss: 0.4311 Accuracy: 0.8637\n",
      "Epoch: 61/200 Train Loss: 0.6128 Accuracy: 0.7990 Time: 6.76770  | Val Loss: 0.4713 Accuracy: 0.8476\n",
      "Epoch: 62/200 Train Loss: 0.6018 Accuracy: 0.8026 Time: 6.70950  | Val Loss: 0.4250 Accuracy: 0.8629\n",
      "Epoch: 63/200 Train Loss: 0.5925 Accuracy: 0.8039 Time: 6.75387  | Val Loss: 0.4130 Accuracy: 0.8701\n",
      "Epoch: 64/200 Train Loss: 0.6015 Accuracy: 0.8030 Time: 6.73148  | Val Loss: 0.4494 Accuracy: 0.8614\n",
      "Epoch: 65/200 Train Loss: 0.5835 Accuracy: 0.8102 Time: 6.71876  | Val Loss: 0.4201 Accuracy: 0.8703\n",
      "Epoch: 66/200 Train Loss: 0.5773 Accuracy: 0.8128 Time: 6.66373  | Val Loss: 0.4111 Accuracy: 0.8678\n",
      "Epoch: 67/200 Train Loss: 0.5714 Accuracy: 0.8117 Time: 6.73933  | Val Loss: 0.4285 Accuracy: 0.8624\n",
      "Epoch: 68/200 Train Loss: 0.5789 Accuracy: 0.8120 Time: 6.74347  | Val Loss: 0.4368 Accuracy: 0.8668\n",
      "Epoch: 69/200 Train Loss: 0.5715 Accuracy: 0.8137 Time: 6.81409  | Val Loss: 0.4585 Accuracy: 0.8589\n",
      "Epoch: 70/200 Train Loss: 0.5650 Accuracy: 0.8175 Time: 6.70082  | Val Loss: 0.4096 Accuracy: 0.8703\n",
      "Epoch: 71/200 Train Loss: 0.5491 Accuracy: 0.8206 Time: 6.75863  | Val Loss: 0.3912 Accuracy: 0.8785\n",
      "Epoch: 72/200 Train Loss: 0.5475 Accuracy: 0.8190 Time: 6.62981  | Val Loss: 0.4245 Accuracy: 0.8678\n",
      "Epoch: 73/200 Train Loss: 0.5520 Accuracy: 0.8218 Time: 6.69756  | Val Loss: 0.4045 Accuracy: 0.8670\n",
      "Epoch: 74/200 Train Loss: 0.5333 Accuracy: 0.8235 Time: 6.76628  | Val Loss: 0.3879 Accuracy: 0.8724\n",
      "Epoch: 75/200 Train Loss: 0.5523 Accuracy: 0.8188 Time: 6.71109  | Val Loss: 0.4192 Accuracy: 0.8639\n",
      "Epoch: 76/200 Train Loss: 0.5208 Accuracy: 0.8251 Time: 6.77922  | Val Loss: 0.4128 Accuracy: 0.8716\n",
      "Epoch: 77/200 Train Loss: 0.5184 Accuracy: 0.8264 Time: 6.71456  | Val Loss: 0.3853 Accuracy: 0.8744\n",
      "Epoch: 78/200 Train Loss: 0.5324 Accuracy: 0.8289 Time: 6.81680  | Val Loss: 0.3842 Accuracy: 0.8782\n",
      "Epoch: 79/200 Train Loss: 0.5355 Accuracy: 0.8274 Time: 6.71252  | Val Loss: 0.4005 Accuracy: 0.8769\n",
      "Epoch: 80/200 Train Loss: 0.5233 Accuracy: 0.8281 Time: 6.72342  | Val Loss: 0.4042 Accuracy: 0.8739\n",
      "Epoch: 81/200 Train Loss: 0.5160 Accuracy: 0.8289 Time: 6.77590  | Val Loss: 0.3884 Accuracy: 0.8825\n",
      "Epoch: 82/200 Train Loss: 0.5159 Accuracy: 0.8252 Time: 6.71470  | Val Loss: 0.3976 Accuracy: 0.8741\n",
      "Epoch: 83/200 Train Loss: 0.5154 Accuracy: 0.8298 Time: 6.75887  | Val Loss: 0.3973 Accuracy: 0.8718\n",
      "Epoch: 84/200 Train Loss: 0.4913 Accuracy: 0.8359 Time: 6.73855  | Val Loss: 0.3957 Accuracy: 0.8777\n",
      "Epoch: 85/200 Train Loss: 0.4994 Accuracy: 0.8403 Time: 6.73448  | Val Loss: 0.3851 Accuracy: 0.8803\n",
      "Epoch: 86/200 Train Loss: 0.4961 Accuracy: 0.8379 Time: 6.83012  | Val Loss: 0.3963 Accuracy: 0.8815\n",
      "Epoch: 87/200 Train Loss: 0.4886 Accuracy: 0.8422 Time: 6.71451  | Val Loss: 0.3709 Accuracy: 0.8874\n",
      "Epoch: 88/200 Train Loss: 0.4904 Accuracy: 0.8415 Time: 6.70023  | Val Loss: 0.3880 Accuracy: 0.8777\n",
      "Epoch: 89/200 Train Loss: 0.4768 Accuracy: 0.8405 Time: 6.68536  | Val Loss: 0.4101 Accuracy: 0.8800\n",
      "Epoch: 90/200 Train Loss: 0.4765 Accuracy: 0.8447 Time: 6.76740  | Val Loss: 0.3844 Accuracy: 0.8818\n",
      "Epoch: 91/200 Train Loss: 0.4801 Accuracy: 0.8405 Time: 6.73372  | Val Loss: 0.3834 Accuracy: 0.8836\n",
      "Epoch: 92/200 Train Loss: 0.4527 Accuracy: 0.8498 Time: 6.84314  | Val Loss: 0.3760 Accuracy: 0.8828\n",
      "Epoch: 93/200 Train Loss: 0.4721 Accuracy: 0.8422 Time: 6.69767  | Val Loss: 0.3479 Accuracy: 0.8887\n",
      "Epoch: 94/200 Train Loss: 0.4698 Accuracy: 0.8416 Time: 6.72374  | Val Loss: 0.3635 Accuracy: 0.8866\n",
      "Epoch: 95/200 Train Loss: 0.4685 Accuracy: 0.8399 Time: 6.77096  | Val Loss: 0.3491 Accuracy: 0.8889\n",
      "Epoch: 96/200 Train Loss: 0.4565 Accuracy: 0.8496 Time: 6.74427  | Val Loss: 0.3536 Accuracy: 0.8897\n",
      "Epoch: 97/200 Train Loss: 0.4497 Accuracy: 0.8485 Time: 6.78586  | Val Loss: 0.3479 Accuracy: 0.8894\n",
      "Epoch: 98/200 Train Loss: 0.4493 Accuracy: 0.8526 Time: 6.78310  | Val Loss: 0.4158 Accuracy: 0.8777\n",
      "Epoch: 99/200 Train Loss: 0.4560 Accuracy: 0.8491 Time: 6.69394  | Val Loss: 0.3571 Accuracy: 0.8943\n",
      "Epoch: 100/200 Train Loss: 0.4465 Accuracy: 0.8504 Time: 6.69149  | Val Loss: 0.3694 Accuracy: 0.8887\n",
      "Epoch: 101/200 Train Loss: 0.4553 Accuracy: 0.8487 Time: 6.77819  | Val Loss: 0.3638 Accuracy: 0.8879\n",
      "Epoch: 102/200 Train Loss: 0.4405 Accuracy: 0.8576 Time: 6.73224  | Val Loss: 0.3422 Accuracy: 0.8948\n",
      "Epoch: 103/200 Train Loss: 0.4473 Accuracy: 0.8530 Time: 6.75961  | Val Loss: 0.3699 Accuracy: 0.8904\n",
      "Epoch: 104/200 Train Loss: 0.4226 Accuracy: 0.8630 Time: 6.72656  | Val Loss: 0.3538 Accuracy: 0.8904\n",
      "Epoch: 105/200 Train Loss: 0.4242 Accuracy: 0.8575 Time: 6.69404  | Val Loss: 0.3511 Accuracy: 0.8922\n",
      "Epoch: 106/200 Train Loss: 0.4419 Accuracy: 0.8565 Time: 6.75821  | Val Loss: 0.3437 Accuracy: 0.8971\n",
      "Epoch: 107/200 Train Loss: 0.4335 Accuracy: 0.8548 Time: 6.69731  | Val Loss: 0.3782 Accuracy: 0.8874\n",
      "Epoch: 108/200 Train Loss: 0.4166 Accuracy: 0.8624 Time: 6.75117  | Val Loss: 0.3366 Accuracy: 0.8948\n",
      "Epoch: 109/200 Train Loss: 0.4142 Accuracy: 0.8641 Time: 6.77299  | Val Loss: 0.3419 Accuracy: 0.8922\n",
      "Epoch: 110/200 Train Loss: 0.4088 Accuracy: 0.8677 Time: 6.72900  | Val Loss: 0.3509 Accuracy: 0.8968\n",
      "Epoch: 111/200 Train Loss: 0.4001 Accuracy: 0.8654 Time: 6.75545  | Val Loss: 0.3424 Accuracy: 0.8999\n",
      "Epoch: 112/200 Train Loss: 0.4286 Accuracy: 0.8595 Time: 6.69613  | Val Loss: 0.3338 Accuracy: 0.8994\n",
      "Epoch: 113/200 Train Loss: 0.4088 Accuracy: 0.8657 Time: 6.67740  | Val Loss: 0.3493 Accuracy: 0.8925\n",
      "Epoch: 114/200 Train Loss: 0.4016 Accuracy: 0.8690 Time: 6.71313  | Val Loss: 0.3400 Accuracy: 0.8989\n",
      "Epoch: 115/200 Train Loss: 0.3989 Accuracy: 0.8713 Time: 6.81305  | Val Loss: 0.3483 Accuracy: 0.8973\n",
      "Epoch: 116/200 Train Loss: 0.3983 Accuracy: 0.8698 Time: 6.67244  | Val Loss: 0.3387 Accuracy: 0.8945\n",
      "Epoch: 117/200 Train Loss: 0.3906 Accuracy: 0.8699 Time: 6.70484  | Val Loss: 0.3527 Accuracy: 0.8927\n",
      "Epoch: 118/200 Train Loss: 0.4003 Accuracy: 0.8734 Time: 6.74738  | Val Loss: 0.3601 Accuracy: 0.8930\n",
      "Epoch: 119/200 Train Loss: 0.3948 Accuracy: 0.8684 Time: 6.68287  | Val Loss: 0.3410 Accuracy: 0.8940\n",
      "Epoch: 120/200 Train Loss: 0.3822 Accuracy: 0.8744 Time: 6.69386  | Val Loss: 0.3395 Accuracy: 0.8945\n",
      "Epoch: 121/200 Train Loss: 0.3770 Accuracy: 0.8753 Time: 6.70081  | Val Loss: 0.3374 Accuracy: 0.8955\n",
      "Epoch: 122/200 Train Loss: 0.3852 Accuracy: 0.8770 Time: 6.76105  | Val Loss: 0.3389 Accuracy: 0.8994\n",
      "Epoch: 123/200 Train Loss: 0.3718 Accuracy: 0.8802 Time: 6.69873  | Val Loss: 0.3320 Accuracy: 0.8976\n",
      "Epoch: 124/200 Train Loss: 0.3616 Accuracy: 0.8793 Time: 6.81313  | Val Loss: 0.3389 Accuracy: 0.8958\n",
      "Epoch: 125/200 Train Loss: 0.3665 Accuracy: 0.8791 Time: 6.70376  | Val Loss: 0.3365 Accuracy: 0.8961\n",
      "Epoch: 126/200 Train Loss: 0.3613 Accuracy: 0.8812 Time: 6.74655  | Val Loss: 0.3360 Accuracy: 0.8966\n",
      "Epoch: 127/200 Train Loss: 0.3695 Accuracy: 0.8792 Time: 6.80607  | Val Loss: 0.3306 Accuracy: 0.8955\n",
      "Epoch: 128/200 Train Loss: 0.3639 Accuracy: 0.8827 Time: 6.73278  | Val Loss: 0.3185 Accuracy: 0.9052\n",
      "Epoch: 129/200 Train Loss: 0.3574 Accuracy: 0.8782 Time: 6.69942  | Val Loss: 0.3505 Accuracy: 0.8927\n",
      "Epoch: 130/200 Train Loss: 0.3520 Accuracy: 0.8826 Time: 6.82347  | Val Loss: 0.3245 Accuracy: 0.9045\n",
      "Epoch: 131/200 Train Loss: 0.3511 Accuracy: 0.8838 Time: 6.70226  | Val Loss: 0.3282 Accuracy: 0.8991\n",
      "Epoch: 132/200 Train Loss: 0.3515 Accuracy: 0.8858 Time: 6.70979  | Val Loss: 0.3355 Accuracy: 0.8955\n",
      "Epoch: 133/200 Train Loss: 0.3563 Accuracy: 0.8817 Time: 6.72680  | Val Loss: 0.3377 Accuracy: 0.8991\n",
      "Epoch: 134/200 Train Loss: 0.3539 Accuracy: 0.8851 Time: 6.72566  | Val Loss: 0.3319 Accuracy: 0.9019\n",
      "Epoch: 135/200 Train Loss: 0.3507 Accuracy: 0.8859 Time: 6.70245  | Val Loss: 0.3315 Accuracy: 0.8981\n",
      "Epoch: 136/200 Train Loss: 0.3501 Accuracy: 0.8872 Time: 6.79794  | Val Loss: 0.3311 Accuracy: 0.8963\n",
      "Epoch: 137/200 Train Loss: 0.3525 Accuracy: 0.8828 Time: 6.83346  | Val Loss: 0.3136 Accuracy: 0.9052\n",
      "Epoch: 138/200 Train Loss: 0.3421 Accuracy: 0.8867 Time: 6.74077  | Val Loss: 0.3177 Accuracy: 0.9045\n",
      "Epoch: 139/200 Train Loss: 0.3395 Accuracy: 0.8857 Time: 6.74288  | Val Loss: 0.3302 Accuracy: 0.8996\n",
      "Epoch: 140/200 Train Loss: 0.3445 Accuracy: 0.8882 Time: 6.72800  | Val Loss: 0.3157 Accuracy: 0.9029\n",
      "Epoch: 141/200 Train Loss: 0.3326 Accuracy: 0.8891 Time: 6.79620  | Val Loss: 0.3241 Accuracy: 0.8999\n",
      "Epoch: 142/200 Train Loss: 0.3366 Accuracy: 0.8895 Time: 6.69948  | Val Loss: 0.3192 Accuracy: 0.9029\n",
      "Epoch: 143/200 Train Loss: 0.3392 Accuracy: 0.8889 Time: 6.72168  | Val Loss: 0.3256 Accuracy: 0.9004\n",
      "Epoch: 144/200 Train Loss: 0.3483 Accuracy: 0.8866 Time: 6.71858  | Val Loss: 0.3200 Accuracy: 0.9039\n",
      "Epoch: 145/200 Train Loss: 0.3371 Accuracy: 0.8891 Time: 6.75200  | Val Loss: 0.3185 Accuracy: 0.9029\n",
      "Epoch: 146/200 Train Loss: 0.3337 Accuracy: 0.8906 Time: 6.75286  | Val Loss: 0.3249 Accuracy: 0.9004\n",
      "Epoch: 147/200 Train Loss: 0.3239 Accuracy: 0.8949 Time: 6.75956  | Val Loss: 0.3235 Accuracy: 0.9017\n",
      "Epoch: 148/200 Train Loss: 0.3305 Accuracy: 0.8909 Time: 6.75287  | Val Loss: 0.3255 Accuracy: 0.8999\n",
      "Epoch: 149/200 Train Loss: 0.3171 Accuracy: 0.8931 Time: 6.77785  | Val Loss: 0.3181 Accuracy: 0.9047\n",
      "Epoch: 150/200 Train Loss: 0.3133 Accuracy: 0.8970 Time: 6.73361  | Val Loss: 0.3164 Accuracy: 0.9042\n",
      "Epoch: 151/200 Train Loss: 0.3286 Accuracy: 0.8928 Time: 6.74427  | Val Loss: 0.3172 Accuracy: 0.9034\n",
      "Epoch: 152/200 Train Loss: 0.3074 Accuracy: 0.8964 Time: 6.77317  | Val Loss: 0.3206 Accuracy: 0.9042\n",
      "Epoch: 153/200 Train Loss: 0.3093 Accuracy: 0.8980 Time: 6.71228  | Val Loss: 0.3248 Accuracy: 0.9006\n",
      "Epoch: 154/200 Train Loss: 0.3083 Accuracy: 0.8999 Time: 6.91299  | Val Loss: 0.3212 Accuracy: 0.9039\n",
      "Epoch: 155/200 Train Loss: 0.3154 Accuracy: 0.8954 Time: 6.67927  | Val Loss: 0.3224 Accuracy: 0.9014\n",
      "Epoch: 156/200 Train Loss: 0.3130 Accuracy: 0.8963 Time: 6.69039  | Val Loss: 0.3258 Accuracy: 0.9001\n",
      "Epoch: 157/200 Train Loss: 0.3042 Accuracy: 0.9023 Time: 6.72828  | Val Loss: 0.3144 Accuracy: 0.9019\n",
      "Epoch: 158/200 Train Loss: 0.3216 Accuracy: 0.8954 Time: 6.75346  | Val Loss: 0.3162 Accuracy: 0.9032\n",
      "Epoch: 159/200 Train Loss: 0.3050 Accuracy: 0.9016 Time: 6.68395  | Val Loss: 0.3231 Accuracy: 0.9009\n",
      "Epoch: 160/200 Train Loss: 0.3051 Accuracy: 0.8987 Time: 6.76926  | Val Loss: 0.3113 Accuracy: 0.9039\n",
      "Epoch: 161/200 Train Loss: 0.3068 Accuracy: 0.8987 Time: 6.78229  | Val Loss: 0.3145 Accuracy: 0.9019\n",
      "Epoch: 162/200 Train Loss: 0.3006 Accuracy: 0.9025 Time: 6.69511  | Val Loss: 0.3157 Accuracy: 0.9050\n",
      "Epoch: 163/200 Train Loss: 0.2917 Accuracy: 0.9035 Time: 6.78905  | Val Loss: 0.3215 Accuracy: 0.9029\n",
      "Epoch: 164/200 Train Loss: 0.3042 Accuracy: 0.8990 Time: 6.74350  | Val Loss: 0.3209 Accuracy: 0.9052\n",
      "Epoch: 165/200 Train Loss: 0.3032 Accuracy: 0.8987 Time: 6.73260  | Val Loss: 0.3147 Accuracy: 0.9083\n",
      "Epoch: 166/200 Train Loss: 0.3137 Accuracy: 0.8977 Time: 6.70301  | Val Loss: 0.3244 Accuracy: 0.9024\n",
      "Epoch: 167/200 Train Loss: 0.3027 Accuracy: 0.9000 Time: 6.75589  | Val Loss: 0.3198 Accuracy: 0.9085\n",
      "Epoch: 168/200 Train Loss: 0.2991 Accuracy: 0.8973 Time: 6.77723  | Val Loss: 0.3141 Accuracy: 0.9050\n",
      "Epoch: 169/200 Train Loss: 0.2901 Accuracy: 0.9070 Time: 6.71020  | Val Loss: 0.3170 Accuracy: 0.9062\n",
      "Epoch: 170/200 Train Loss: 0.3043 Accuracy: 0.8995 Time: 6.80351  | Val Loss: 0.3173 Accuracy: 0.9057\n",
      "Epoch: 171/200 Train Loss: 0.2922 Accuracy: 0.9034 Time: 6.74187  | Val Loss: 0.3160 Accuracy: 0.9050\n",
      "Epoch: 172/200 Train Loss: 0.3016 Accuracy: 0.9017 Time: 6.70438  | Val Loss: 0.3098 Accuracy: 0.9078\n",
      "Epoch: 173/200 Train Loss: 0.2835 Accuracy: 0.9082 Time: 6.77234  | Val Loss: 0.3172 Accuracy: 0.9047\n",
      "Epoch: 174/200 Train Loss: 0.2951 Accuracy: 0.9036 Time: 6.83431  | Val Loss: 0.3133 Accuracy: 0.9078\n",
      "Epoch: 175/200 Train Loss: 0.2859 Accuracy: 0.9074 Time: 6.77715  | Val Loss: 0.3152 Accuracy: 0.9062\n",
      "Epoch: 176/200 Train Loss: 0.2821 Accuracy: 0.9061 Time: 6.79065  | Val Loss: 0.3096 Accuracy: 0.9065\n",
      "Epoch: 177/200 Train Loss: 0.3010 Accuracy: 0.9041 Time: 6.73499  | Val Loss: 0.3125 Accuracy: 0.9070\n",
      "Epoch: 178/200 Train Loss: 0.2939 Accuracy: 0.9018 Time: 6.69293  | Val Loss: 0.3125 Accuracy: 0.9060\n",
      "Epoch: 179/200 Train Loss: 0.2879 Accuracy: 0.9039 Time: 6.80315  | Val Loss: 0.3132 Accuracy: 0.9045\n",
      "Epoch: 180/200 Train Loss: 0.2839 Accuracy: 0.9071 Time: 6.75862  | Val Loss: 0.3144 Accuracy: 0.9078\n",
      "Epoch: 181/200 Train Loss: 0.2981 Accuracy: 0.9008 Time: 6.71970  | Val Loss: 0.3106 Accuracy: 0.9090\n",
      "Epoch: 182/200 Train Loss: 0.2745 Accuracy: 0.9067 Time: 6.69384  | Val Loss: 0.3106 Accuracy: 0.9057\n",
      "Epoch: 183/200 Train Loss: 0.2865 Accuracy: 0.9051 Time: 6.71523  | Val Loss: 0.3132 Accuracy: 0.9055\n",
      "Epoch: 184/200 Train Loss: 0.2895 Accuracy: 0.9063 Time: 6.81689  | Val Loss: 0.3130 Accuracy: 0.9055\n",
      "Epoch: 185/200 Train Loss: 0.2878 Accuracy: 0.9034 Time: 6.68420  | Val Loss: 0.3119 Accuracy: 0.9060\n",
      "Epoch: 186/200 Train Loss: 0.2866 Accuracy: 0.9048 Time: 6.77985  | Val Loss: 0.3100 Accuracy: 0.9047\n",
      "Epoch: 187/200 Train Loss: 0.2786 Accuracy: 0.9079 Time: 6.80204  | Val Loss: 0.3156 Accuracy: 0.9070\n",
      "Epoch: 188/200 Train Loss: 0.2830 Accuracy: 0.9084 Time: 6.71811  | Val Loss: 0.3128 Accuracy: 0.9062\n",
      "Epoch: 189/200 Train Loss: 0.2742 Accuracy: 0.9098 Time: 6.79531  | Val Loss: 0.3138 Accuracy: 0.9052\n",
      "Epoch: 190/200 Train Loss: 0.2731 Accuracy: 0.9107 Time: 6.72276  | Val Loss: 0.3112 Accuracy: 0.9055\n",
      "Epoch: 191/200 Train Loss: 0.2874 Accuracy: 0.9046 Time: 6.73186  | Val Loss: 0.3092 Accuracy: 0.9093\n",
      "Epoch: 192/200 Train Loss: 0.2628 Accuracy: 0.9117 Time: 6.72169  | Val Loss: 0.3135 Accuracy: 0.9060\n",
      "Epoch: 193/200 Train Loss: 0.2913 Accuracy: 0.9014 Time: 6.69389  | Val Loss: 0.3131 Accuracy: 0.9080\n",
      "Epoch: 194/200 Train Loss: 0.2814 Accuracy: 0.9095 Time: 6.79026  | Val Loss: 0.3100 Accuracy: 0.9062\n",
      "Epoch: 195/200 Train Loss: 0.2910 Accuracy: 0.9072 Time: 6.72415  | Val Loss: 0.3117 Accuracy: 0.9075\n",
      "Epoch: 196/200 Train Loss: 0.2784 Accuracy: 0.9093 Time: 6.76097  | Val Loss: 0.3118 Accuracy: 0.9065\n",
      "Epoch: 197/200 Train Loss: 0.2786 Accuracy: 0.9073 Time: 6.68315  | Val Loss: 0.3136 Accuracy: 0.9060\n",
      "Epoch: 198/200 Train Loss: 0.2769 Accuracy: 0.9071 Time: 6.68824  | Val Loss: 0.3104 Accuracy: 0.9055\n",
      "Epoch: 199/200 Train Loss: 0.2958 Accuracy: 0.9000 Time: 6.70225  | Val Loss: 0.3118 Accuracy: 0.9047\n",
      "Epoch: 200/200 Train Loss: 0.2839 Accuracy: 0.9054 Time: 6.76573  | Val Loss: 0.3105 Accuracy: 0.9055\n",
      "#Parameter: 1930066 Accuracy: 0.9054777070063694\n"
     ]
    }
   ],
   "source": [
    "net = ShuffleNetV1(\n",
    "    num_classes=10,\n",
    "    group=8,\n",
    "    width_multiplier=0.5,\n",
    "    dropout=0,\n",
    ").to(DEVICE)\n",
    "width_multiplier_50 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    net,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "223d0790",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 499258\n",
      "Epoch: 1/200 Train Loss: 2.1346 Accuracy: 0.2261 Time: 5.52261  | Val Loss: 1.8464 Accuracy: 0.3213\n",
      "Epoch: 2/200 Train Loss: 1.8443 Accuracy: 0.3662 Time: 5.62801  | Val Loss: 1.5167 Accuracy: 0.4841\n",
      "Epoch: 3/200 Train Loss: 1.6846 Accuracy: 0.4339 Time: 5.45363  | Val Loss: 1.3995 Accuracy: 0.5312\n",
      "Epoch: 4/200 Train Loss: 1.6050 Accuracy: 0.4549 Time: 5.61594  | Val Loss: 1.2271 Accuracy: 0.5926\n",
      "Epoch: 5/200 Train Loss: 1.5383 Accuracy: 0.4814 Time: 5.50787  | Val Loss: 1.2427 Accuracy: 0.5832\n",
      "Epoch: 6/200 Train Loss: 1.4665 Accuracy: 0.5117 Time: 5.53641  | Val Loss: 1.2087 Accuracy: 0.5967\n",
      "Epoch: 7/200 Train Loss: 1.4198 Accuracy: 0.5243 Time: 5.53885  | Val Loss: 1.0875 Accuracy: 0.6423\n",
      "Epoch: 8/200 Train Loss: 1.3901 Accuracy: 0.5342 Time: 5.61542  | Val Loss: 1.1810 Accuracy: 0.6173\n",
      "Epoch: 9/200 Train Loss: 1.3628 Accuracy: 0.5496 Time: 5.59278  | Val Loss: 0.9550 Accuracy: 0.6915\n",
      "Epoch: 10/200 Train Loss: 1.3108 Accuracy: 0.5720 Time: 5.44258  | Val Loss: 1.0468 Accuracy: 0.6724\n",
      "Epoch: 11/200 Train Loss: 1.3000 Accuracy: 0.5681 Time: 5.43609  | Val Loss: 0.8971 Accuracy: 0.7121\n",
      "Epoch: 12/200 Train Loss: 1.2629 Accuracy: 0.5869 Time: 5.50562  | Val Loss: 1.0213 Accuracy: 0.6688\n",
      "Epoch: 13/200 Train Loss: 1.2234 Accuracy: 0.5997 Time: 5.53411  | Val Loss: 0.9010 Accuracy: 0.7108\n",
      "Epoch: 14/200 Train Loss: 1.1981 Accuracy: 0.6021 Time: 5.49101  | Val Loss: 0.8963 Accuracy: 0.7129\n",
      "Epoch: 15/200 Train Loss: 1.1906 Accuracy: 0.6062 Time: 5.78755  | Val Loss: 0.8529 Accuracy: 0.7205\n",
      "Epoch: 16/200 Train Loss: 1.1662 Accuracy: 0.6195 Time: 5.52413  | Val Loss: 0.8184 Accuracy: 0.7361\n",
      "Epoch: 17/200 Train Loss: 1.1427 Accuracy: 0.6226 Time: 5.58842  | Val Loss: 0.9136 Accuracy: 0.7039\n",
      "Epoch: 18/200 Train Loss: 1.1227 Accuracy: 0.6271 Time: 5.49442  | Val Loss: 0.9081 Accuracy: 0.7017\n",
      "Epoch: 19/200 Train Loss: 1.1121 Accuracy: 0.6354 Time: 5.50745  | Val Loss: 0.8306 Accuracy: 0.7424\n",
      "Epoch: 20/200 Train Loss: 1.1039 Accuracy: 0.6317 Time: 5.47844  | Val Loss: 0.7951 Accuracy: 0.7460\n",
      "Epoch: 21/200 Train Loss: 1.0740 Accuracy: 0.6466 Time: 5.66904  | Val Loss: 0.7528 Accuracy: 0.7659\n",
      "Epoch: 22/200 Train Loss: 1.0650 Accuracy: 0.6483 Time: 5.48620  | Val Loss: 0.7259 Accuracy: 0.7689\n",
      "Epoch: 23/200 Train Loss: 1.0603 Accuracy: 0.6568 Time: 5.48329  | Val Loss: 0.7547 Accuracy: 0.7562\n",
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      "Epoch: 187/200 Train Loss: 0.4366 Accuracy: 0.8573 Time: 5.50606  | Val Loss: 0.3431 Accuracy: 0.8961\n",
      "Epoch: 188/200 Train Loss: 0.4257 Accuracy: 0.8615 Time: 5.45044  | Val Loss: 0.3431 Accuracy: 0.8966\n",
      "Epoch: 189/200 Train Loss: 0.4350 Accuracy: 0.8552 Time: 5.47591  | Val Loss: 0.3416 Accuracy: 0.8976\n",
      "Epoch: 190/200 Train Loss: 0.4221 Accuracy: 0.8631 Time: 5.59133  | Val Loss: 0.3400 Accuracy: 0.8978\n",
      "Epoch: 191/200 Train Loss: 0.4249 Accuracy: 0.8593 Time: 5.47088  | Val Loss: 0.3438 Accuracy: 0.8932\n",
      "Epoch: 192/200 Train Loss: 0.4353 Accuracy: 0.8589 Time: 5.54967  | Val Loss: 0.3428 Accuracy: 0.8961\n",
      "Epoch: 193/200 Train Loss: 0.4281 Accuracy: 0.8602 Time: 5.51107  | Val Loss: 0.3421 Accuracy: 0.8991\n",
      "Epoch: 194/200 Train Loss: 0.4362 Accuracy: 0.8573 Time: 5.53308  | Val Loss: 0.3398 Accuracy: 0.8968\n",
      "Epoch: 195/200 Train Loss: 0.4358 Accuracy: 0.8611 Time: 5.51113  | Val Loss: 0.3443 Accuracy: 0.8950\n",
      "Epoch: 196/200 Train Loss: 0.4288 Accuracy: 0.8555 Time: 5.57653  | Val Loss: 0.3413 Accuracy: 0.8966\n",
      "Epoch: 197/200 Train Loss: 0.4446 Accuracy: 0.8556 Time: 5.75532  | Val Loss: 0.3424 Accuracy: 0.8968\n",
      "Epoch: 198/200 Train Loss: 0.4356 Accuracy: 0.8573 Time: 5.60636  | Val Loss: 0.3421 Accuracy: 0.8973\n",
      "Epoch: 199/200 Train Loss: 0.4244 Accuracy: 0.8611 Time: 5.55955  | Val Loss: 0.3420 Accuracy: 0.8958\n",
      "Epoch: 200/200 Train Loss: 0.4265 Accuracy: 0.8595 Time: 5.45729  | Val Loss: 0.3453 Accuracy: 0.8950\n",
      "#Parameter: 499258 Accuracy: 0.8950318471337579\n"
     ]
    }
   ],
   "source": [
    "net = ShuffleNetV1(\n",
    "    num_classes=10,\n",
    "    group=8,\n",
    "    width_multiplier=0.25,\n",
    "    dropout=0,\n",
    ").to(DEVICE)\n",
    "width_multiplier_25 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    net,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4dd9ad68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FfHw8qampvrLU1FQuuOACunXrxrJlyyqVJycnVzv8LCUlhc6dOxMcHMz48ePZv39/pWOTJ09m3bp1GIaBYRikpKT4jmdnZzN+/HiCg4Pp0aMH8+fPrzXmkJAQXnvtNW666Sbi4uKO+u9ARESkNVNbf2xQ4i0i0sSK7c4aX6UOV6PWbYiRI0eyaNEi3+dFixYxcuRIkpKSfOV2u53ly5eTnJxc5fwff/yR66+/nltvvZW0tDSSk5N54oknfMcvv/xy7rnnHvr160dGRgYZGRlcfvnlvuOTJ0/msssuY/369Zx99tlcddVVHDhwoEH3IiIi4g/N2dY3pL1XW+9/Fn8HcKxxHjhA1jPP4PF46PDss/4OR0Ragb6PflXjseRe7Zh13Sm+zyc+/g0lhzW6FU5NjGLOX4f5Pp/+zCIOFNkr1dn29Dn1jm/kyJHcfffdOJ1OSkpKWLt2LWeccQYul4uXXnoJgBUrVlBSUkJycjI7duyodP60adMYM2YMDzzwAAA9e/Zk2bJlLFiwAICgoCBCQ0OxWCzVPrWeMGECV1xxBQBPPvkk06dPZ+XKlYwdO7be9yIiIuIPzdnWQ/3be7X1/qce78PkFxST98l8cj77wt+hiIg0i+TkZIqKili1ahVLly6lZ8+exMTEkJSUxKpVqygqKiI1NZXOnTvTtWvXKudv3LiRYcOGVSo7/HNtBg4c6HsfEhJCWFgYWVlZAPTr14/Q0FBCQ0MZN25cA+9QpKrnF27mrg/WsmVvgb9DERFpcmrr/U893ofxWG0AmN0unHYHlgCrnyMSkZbu1yljajxmOmzRkp8eqXkBkcPrfv/3qkPBGqJ79+507NiRRYsWkZOTQ1JSEgBxcXEkJibyww8/sGjRIs4888xqz/d4PEf1/VZr5f/PGoaB2+0G4IsvvsDhcADep+kijeXbjVn8mpHPRSd0pGdsmL/DEZEWTm197dTWK/GuIiDk4A+7rKhEibeIHLXggLr/r7ap6h5JxUIqOTk53Hfffb7ypKQkvvrqK1asWMF1111X7bl9+/ZlxYoVlcoO/xwQEIDLVf2wutp06dKl3ueI1IXV4h3053C5/RyJiLQGauvV1h+JEu/DBLpyfe9Li4oIaRPuv2BERJpJcnIyt912Gw6Hw/cUHLyN8f/93/9RWlpa7WIrAHfccQfDhw/n2Wef5cILL2ThwoW+OV8VEhISSE9PJy0tjY4dOxIWFobNZmtwvL/++it2u50DBw5QUFBAWloaQKPtHyqtX4DZ26ukxFtEjhdq6/1Lc7wPY7WYMUzeoRRlhcVHqC0i0jokJydTUlJC9+7diY2N9ZUnJSVRUFBAt27d6NSpU7XnDh06lDfffJPp06czePBgFi5cyMMPP1ypzsUXX8zYsWNJTk6mXbt2zJ49+6jiPfvssxkyZAiffvopqampDBkyhCFDhhzVNeX4YjV7fwWyu45u+KSISEuhtt6/DM/RDtg/RuTn5xMREUFeXh7h4Q3vpXYX72fLqcPxOEwEvzubLicNbrwgRaTVKi0tJT09ncTERAIDA/0djjSjmn72jdUuyUGN+Xf6l7dWsnjLPp6/dBAXn9ixkSIUkdZMbf3xq7affV3bJvV4H+aAs5T9tvKn4AV5fo5GREREmkJFj7eGmouISHNQ4n2YQFs49vKZ7yX5x9em7iIiIseLAIvmeIuISPPR4mqHsVkCfYl3jLXUv8GIiIhIk3jqooE8OX5Ao64YLCIiUhO1NoexmCw4yv9WXMX5/g1GREREmkREkLYLFRGR5qOh5ocxDANneeJtL9QcbxEROb489dRTnHzyyYSFhRETE8OFF17I5s2bj3je4sWLOfHEEwkMDKRr167MmDGjSp25c+fSt29fbDYbffv25eOPP26KWxARETnmKPGuhqs88c7cl+3fQERERJrZ4sWLue2221ixYgVff/01TqeTs846i6KiohrPSU9P5+yzz2bEiBGsXbuWBx98kDvuuIO5c+f66ixfvpzLL7+ca665hnXr1nHNNddw2WWX8eOPPzbHbVXx+foMJn30Mws2ZPrl+0VE5PiioebVcFgMwEN+voaai4jI8WXBggWVPs+aNYuYmBh++uknzjjjjGrPmTFjBp07d2bq1KkA9OnTh9WrV/Pcc89x8cUXAzB16lRGjx7NpEmTAJg0aRKLFy9m6tSpR73Xa0Os3ZHD7JU7iAiyMrZ/XLN/v4iIHF/U412NSMO70qmtzOXnSERERPwrL8877SoqKqrGOsuXL+ess86qVDZmzBhWr16Nw+Gotc6yZcsaOeK6sVq0nZiIiDQf9XhXI8FjxUMZoXZ/RyIiIuI/Ho+HiRMncvrpp9O/f/8a62VmZhIbG1upLDY2FqfTSXZ2Nu3bt6+xTmZmzUO9y8rKKCsr831uzJFo2sdbRESak3q8q+G2eJ9HGGUlfo5EROTYk5qaimEY5Obm1lgnJSWFyMjII17LMAzmzZvXaLFJ47r99ttZv359nYaCG+WjxSp4PJ4q5dXVObzsUE899RQRERG+V6dOneoTfq0CzNrHW0SkJmrrG58S72o4yxNvV6kSbxFp3WbMmEFYWBhOp9NXVlhYiNVqZcSIEZXqLl26FMMwiI+PJyMjg4iIiDp/z2OPPcbgwYMbK2x+/vlnkpKSCAoKokOHDkyZMsWX6FWn4heI6l6rVq3y1avueHWrcx8P/va3vzF//nwWLVpEx44da60bFxdXpec6KysLi8VCdHR0rXUO7wU/1KRJk8jLy/O9du7c2cC7qaqix9vurPnfjYhIa6C2/tho6zXUvBo/hpRwOrDPfsDfoYiINKnk5GQKCwtZvXo1Q4cOBbyNblxcHKtWraK4uJjg4GDA26DFx8fTs2dPf4ZMfn4+o0ePJjk5mVWrVrFlyxYmTJhASEgI99xzT7XnDB8+nIyMjEpljzzyCN988w0nnXRSpfJZs2YxduxY3+f6/NLRGng8Hv72t7/x8ccfk5qaSmJi4hHPGTZsGJ9++mmlsoULF3LSSSdhtVp9db7++mvuvvvuSnWGDx9e43VtNhs2m62Bd1I7DTUXkeOF2vpjo61Xj3c13BYzAIZDk7xFpHXr1asX8fHxpKam+spSU1O54IIL6NatW6WFr1JTU0lOTq52+FlKSgqdO3cmODiY8ePHs3///krHJk+ezLp163xPllNSUnzHs7OzGT9+PMHBwfTo0YP58+fXGvN7771HaWkpKSkp9O/fn4suuogHH3yQF154ocYn4QEBAcTFxfle0dHRzJ8/n+uvv77KUOfIyMhKdYOCgurwN9l63Hbbbbz77ru8//77hIWFkZmZSWZmJiUlB0eBTZo0iWuvvdb3+ZZbbmH79u1MnDiRjRs38tZbbzFz5kzuvfdeX50777yThQsX8swzz7Bp0yaeeeYZvvnmG+66667mvD0fLa4mIscLtfXHRluvxLsatmDv0/kw/e2IyNHweMBe1PyvWoZhVWfkyJEsWrTI93nRokWMHDmSpKQkX7ndbmf58uUkJydXOf/HH3/k+uuv59ZbbyUtLY3k5GSeeOIJ3/HLL7+ce+65h379+pGRkUFGRgaXX3657/jkyZO57LLLWL9+PWeffTZXXXUVBw7UPOJo+fLlJCUlVeoJHTNmDHv27GHbtm11uuf58+eTnZ3NhAkTqhy7/fbbadu2LSeffDIzZszA7T6+ErPXXnuNvLw8Ro4cSfv27X2vOXPm+OpkZGSwY8cO3+fExES++OILUlNTGTx4MI8//jgvvfSSbysx8PZEfPDBB8yaNYuBAweSkpLCnDlzOPXUU5v1/ipcODieZQ+cydMXDfTL94tIK+Gvtr6e7b3a+sr80dZrqHk1TDZv4o3DWXtFEZHaOIrhyfjm/94H90BASJ2rjxw5krvvvhun00lJSQlr167ljDPOwOVy8dJLLwGwYsUKSkpKSE5OrpRwAUybNo0xY8bwwAMPANCzZ0+WLVvm2w86KCiI0NBQLBYLcXFV90ueMGECV1xxBQBPPvkk06dPZ+XKlZWGgB0qMzOThISESmUV84QzMzPrNDR65syZjBkzpspiXY8//jijRo0iKCiIb7/9lnvuuYfs7GwefvjhI16ztaht/lyFQ3sxKiQlJbFmzZpaz7vkkku45JJLGhpaowoLtBIWaPV3GCLS0vmrrYd6tfdq6w/yV1uvPt1qmMrno2HXPt4i0volJydTVFTEqlWrWLp0KT179iQmJoakpCRWrVpFUVERqampdO7cma5du1Y5f+PGjQwbNqxS2eGfazNw4MEex5CQEMLCwsjKygKgX79+hIaGEhoayrhx43z16rKCdk127drFV199xQ033FDl2MMPP8ywYcMYPHgw99xzD1OmTOFf//pXne9FRETkWKS2/iB/tfX16vF+6qmn+Oijj9i0aRNBQUEMHz6cZ555hl69etV63uLFi5k4cSK//PIL8fHx3H///dxyyy2V6sydO5dHHnmE33//nW7duvHPf/6T8ePH1/+OGkGZyTvH21nm8Mv3i0grYQ32Po32x/fWQ/fu3enYsSOLFi0iJyeHpKQkwLsKdWJiIj/88AOLFi3izDPPrPb8uvSQ1hqutXKvo2EYviFfX3zxBQ6H9//FFfOvalodG6h1hewKs2bNIjo6mvPPP/+IdYcOHUp+fj579+6t07Wl5dicWcB/V+8kLiKQG0dU/SVTRKRO/NXWV3x3Hamtr1lztfX1SrwXL17Mbbfdxsknn4zT6eShhx7irLPO4tdffyUkpPphDunp6Zx99tncdNNNvPvuu/zwww/ceuuttGvXzjf3a/ny5Vx++eU8/vjjjB8/no8//pjLLruM77//3i9zv8qM8sXV7MfXvD4RaWSGUa8h3/5UsZBKTk4O9913n688KSmJr776ihUrVnDddddVe27fvn1ZsWJFpbLDPwcEBOBy1X8UUZcuXaqUDRs2jAcffBC73U5AQADgXR07Pj6+yrC0w3k8HmbNmsW1115b5ZeA6qxdu5bAwMA67VMqLcuunGLe/D6dQR0jlHiLSMOprfdRW1+7eg01X7BgARMmTKBfv34MGjSIWbNmsWPHDn766acaz5kxYwadO3dm6tSp9OnThxtvvJHrr7+e5557zldn6tSpjB49mkmTJtG7d28mTZrEqFGjmDp1aoNv7GhEB3r3HA1Wh7eIHCeSk5P5/vvvSUtL8z0FB29j/MYbb1BaWlrtYisAd9xxBwsWLODZZ59ly5YtvPzyy745XxUSEhJIT08nLS2N7OxsysrKGhzrlVdeic1mY8KECWzYsIGPP/6YJ598kokTJ/qGn61cuZLevXuze/fuSud+9913pKenVzv07NNPP+WNN95gw4YN/P7777z55ps89NBD3HzzzU22pZX4j28fb5f28RaR44Paev+29Uc1xzsvLw+AqKioGussX76cs846q1LZmDFjWL16tW9IQU11Dl3a/nBlZWXk5+dXejWWniEJAIQq8RaR40RycjIlJSV079690jCrpKQkCgoK6NatW5XFSSoMHTqUN998k+nTpzN48GAWLlxYZYGSiy++mLFjx5KcnEy7du2YPXt2g2ONiIjg66+/ZteuXZx00knceuutTJw4kYkTJ/rqFBcXs3nzZl87U2HmzJkMHz6cPn36VLmu1Wrl1VdfZdiwYQwcOJBp06YxZcoUnn/++QbHKscu7eMtIscbtfX+besNTwMH7Hs8Hi644AJycnJYunRpjfV69uzJhAkTePDBB31ly5Yt47TTTmPPnj20b9+egIAAUlJSuPLKK3113n//fa677roan5Q89thjTJ48uUp5Xl4e4eHhDbkln7Wv/5PAF9/FE2Ki70+/HNW1ROT4UFpaSnp6OomJiQQGBvo7HGlGNf3s8/PziYiIaJR2Sbwa8+/0p+0HuPi15SREB5N6X/U9PCIih1Jbf/yq7Wdf17apwT3et99+O+vXr6/Tk4y6rEhXXZ3aVqybNGkSeXl5vtfOnTvrE36tTIHl8zScegouIiLSGh3s8dZQcxERaXoN2sf7b3/7G/Pnz2fJkiV07Nix1ro1rUhnsViIjo6utU5tq8rZbLYmG4e/jj2cDGhtNRERkdbJYqqY463GXkREml69erw9Hg+33347H330Ed99912dNi4fNmwYX3/9daWyhQsXctJJJ/lWmaupzvDhw+sTXqOxhIR5/3T65etFRESkiUVv+YBbzZ8Q5czydygiInIcqFfifdttt/Huu+/y/vvvExYWRmZmJpmZmZSUlPjqTJo0iWuvvdb3+ZZbbmH79u1MnDiRjRs38tZbbzFz5kzuvfdeX50777yThQsX8swzz7Bp0yaeeeYZvvnmG+66666jv8MG6BDXDgCzBzwOrbAmIiLS2rTb8Ab3W+fwzoUx/g5FRESOA/VKvF977TXy8vIYOXIk7du3973mzJnjq5ORkcGOHTt8nxMTE/niiy9ITU1l8ODBPP7447z00ku+PbwBhg8fzgcffMCsWbMYOHAgKSkpzJkzxy97eAOEhLXxvXeX2f0Sg4iIiDQdk9m7L2xs6FFt8CIiIlIn9ZrjXZcF0FNSUqqUJSUlsWbNmlrPu+SSS7jkkkvqE06TCQyN9L33lJVCaIj/ghEREZHGZyr/FcileWUiItL09Ji3Gk4jGHt5e+w5ZBi9iIiItA7u8sR77qp0nFpgTUREmpgS72oYljBf4u0qyvdvMCIiItLoPCbvAq/f/LJbK5uLiEiTU+JdjcjQthhm77B6e0Gen6MRERGRxmaYvYm3FRcOp/byFhGRpqXEuxpxYXFE4gLAnp/r32BERI4xqampGIZBbm5ujXVSUlKIjIw84rUMw2DevHmNFptIXVUk3hac6vEWETmM2vrGp8S7GgEWM5i97x0FBf4NRkSkCc2YMYOwsDCczoMLTBUWFmK1WhkxYkSlukuXLsUwDOLj48nIyCAiIqLO3/PYY48xePDgxgqbn3/+maSkJIKCgujQoQNTpkw54gKgCQkJGIZR6fXAAw80WkzSsjzg2ctl8XEUBu3HocRbRFoxtfXHRltfr1XNjxeGYeAxGwA4i5V4i0jrlZycTGFhIatXr2bo0KGAt9GNi4tj1apVFBcXExwcDHiffsfHx9OzZ09/hkx+fj6jR48mOTmZVatWsWXLFiZMmEBISAj33HNPredOmTKFm266yfc5NDS0qcOVY1Q6djbaAhhqKlPiLSKtmtr6Y6OtV493DX4J8XZ5H8jL9HMkIiJNp1evXsTHx5OamuorS01N5YILLqBbt24sW7asUnlycnK1w89SUlLo3LkzwcHBjB8/nv3791c6NnnyZNatW+d7+nzo1pPZ2dmMHz+e4OBgevTowfz582uN+b333qO0tJSUlBT69+/PRRddxIMPPsgLL7xwxCfhYWFhxMXF+V7HSmMszc9a8SuQ4VLiLSKtmtr6Y6OtV+JdA0f5WAC7VjUXkaNU7Ciu8VXmKqtz3VJn6RHrNsTIkSNZtGiR7/OiRYsYOXIkSUlJvnK73c7y5ctJTk6ucv6PP/7I9ddfz6233kpaWhrJyck88cQTvuOXX34599xzD/369SMjI4OMjAwuv/xy3/HJkydz2WWXsX79es4++2yuuuoqDhw4UGO8y5cvJykpCZvN5isbM2YMe/bsYdu2bbXe6zPPPEN0dDSDBw/mn//8J3a7/Yh/P9I6WQzvA3bDcGDX4moicpSas61vSHuvtt7/NNS8Bi6LAXgIdpcdsa6ISG1Off/UGo+N6DCCV//0qu/zyA9HUuIsqbbuSbEnMWvsLN/nsXPHklOWU6nOz3/5ud7xjRw5krvvvhun00lJSQlr167ljDPOwOVy8dJLLwGwYsUKSkpKSE5OZseOHZXOnzZtGmPGjPHNoerZsyfLli1jwYIFAAQFBREaGorFYiEuLq7K90+YMIErrrgCgCeffJLp06ezcuVKxo4dW228mZmZJCQkVCqLjY31HUtMTKz2vDvvvJMTTjiBNm3asHLlSiZNmkR6ejpvvvlmHf+mpDWxGt6+h5G9oujaLsTP0YhIS9ecbT3Uv71XW+//tl493jVwWb1zvN1l1f9HISLSWiQnJ1NUVMSqVatYunQpPXv2JCYmhqSkJFatWkVRURGpqal07tyZrl27Vjl/48aNDBs2rFLZ4Z9rM3DgQN/7kJAQwsLCyMrKAqBfv36EhoYSGhrKuHHjfPUMw6h0jYphZ4eXH+ruu+8mKSmJgQMHcuONNzJjxgxmzpxZaaicHD8Cynu8gwMg0Gr2czQiIk1Lbb3/23r1eNfAbfH+QF0lDRu6KSJS4ccrf6zxmNlU+Rf+1MtSa6xrMio/K11w8YKjiqtC9+7d6dixI4sWLSInJ4ekpCQA4uLiSExM5IcffmDRokWceeaZ1Z5/pLlWR2K1Wit9NgwDt9s75/aLL77A4XAA3qfpFXFlZlZef6Oi8a54Gl4XFQvM/Pbbb0RHRzcseGmxrOWJt8Pj8HMkItIaqK2vndp6Jd41cpWval5UWOTnSESkpQu2Bvu97pFULKSSk5PDfffd5ytPSkriq6++YsWKFVx33XXVntu3b19WrFhRqezwzwEBAbhcrnrH1aVLlyplw4YN48EHH8RutxMQEADAwoULiY+PrzIsrTZr164FoH379vWOS1q+cLONqFIXu/YVsi27iIS2Gm4uIg2ntl5t/ZFoqHkN7OUPpkqLNNRcRFq/5ORkvv/+e9LS0nxPwcHbGL/xxhuUlpZWu9gKwB133MGCBQt49tln2bJlCy+//LJvzleFhIQE0tPTSUtLIzs7m7Kyhq+fceWVV2Kz2ZgwYQIbNmzg448/5sknn2TixIm+4WcrV66kd+/e7N69G/Au0vLiiy+SlpZGeno6H374IX/96185//zz6dy5c4NjkZbr8cgTWLxjN0F/WPl9X6G/wxERaXJq6/1LiXcNQg3vcAibXVuMiEjrl5ycTElJCd27d680hCspKYmCggK6detGp06dqj136NChvPnmm0yfPp3BgwezcOFCHn744Up1Lr74YsaOHUtycjLt2rVj9uzZDY41IiKCr7/+ml27dnHSSSdx6623MnHiRCZOnOirU1xczObNm31D12w2G3PmzGHkyJH07duXRx99lJtuuumo4pCWbecbK/j98xgiDxRoOzEROS6orfcvw3O0A/aPEfn5+URERJCXl0d4ePhRX2/1VcmE/JSJe0gC/WZ/2QgRikhrVlpaSnp6OomJiQQGBvo7HGlGNf3sG7tdksb9O/0jeShlGXlsGtEF8+1vcf6g+EaKUkRaK7X1x6/afvZ1bZvU410Dl9U7l4BjZN83ERERaTw5JicAOwLzcTjV4y0iIk1LiXcN3OWT+HFotVMREZHWprR8edlSw6Gh5iIi0uSUeNfgp6BcAPY6teCKiIhIq2Px/gpkdnuUeIuISJNT4l0Dh9W7rLnhqP+S+CIiInJs81i87bzJ7cLuahXL3YiIyDFMiXcN2kR69/O0utUYi4iItDrlD9gjAiycN9D/+7uKiEjrpsS7BoHB3s3qzQ4l3iIiIq1OeeJtcbmJCdfqxCIi0rSUeNfAXL5MvEkrnYqIiLQ+Fu/qah7N7xYRkWagxLsGxVgBMNTjLSIi0uqYArztPGVuVvyx37/BiIhIq6fEuwY5bu+TcIsSbxERkVana5h3XvfgHPj6171+jkZERFo7Jd41CA6LAcBmB49HybeISIXU1FQMwyA3N7fGOikpKURGRh7xWoZhMG/evEaLTaSujIAAAEwubScmInI4tfWNT4l3DXpEnwCAyQ2ekhI/RyMi0jRmzJhBWFgYTqfTV1ZYWIjVamXEiBGV6i5duhTDMIiPjycjI4OIiIg6f89jjz3G4MGDGytsfv75Z5KSkggKCqJDhw5MmTKl1oek27Zt44YbbiAxMZGgoCC6devGP/7xD+x2e6V6hmFUec2YMaPR4pZjhy/xdruVeItIq6a2/tho6y1N/g0tlBEaDoYHPAauggJM5auci4i0JsnJyRQWFrJ69WqGDh0KeBvduLg4Vq1aRXFxMcHl//9LTU0lPj6enj17+jNk8vPzGT16NMnJyaxatYotW7YwYcIEQkJCuOeee6o9Z9OmTbjdbl5//XW6d+/Ohg0buOmmmygqKuK5556rVHfWrFmMHTvW97k+v3RIy5HlKcYMbLG6sTs1sk1EWi+19cdGW68e7xqYg8IwB3ifgLvy8vwcjYhI0+jVqxfx8fGkpqb6ylJTU7ngggvo1q0by5Ytq1SenJxc7fCzlJQUOnfuTHBwMOPHj2f//v2Vjk2ePJl169b5niynpKT4jmdnZzN+/HiCg4Pp0aMH8+fPrzXm9957j9LSUlJSUujfvz8XXXQRDz74IC+88EKNT8LHjh3LrFmzOOuss+jatSvnn38+9957Lx999FGVupGRkcTFxfleQUFBR/hblJaotHxttSID9XiLSKumtv7YaOuVeNdgp2sfGUHePT7dSrxFpAE8Hg/u4uJmf9V3XYqRI0eyaNEi3+dFixYxcuRIkpKSfOV2u53ly5eTnJxc5fwff/yR66+/nltvvZW0tDSSk5N54oknfMcvv/xy7rnnHvr160dGRgYZGRlcfvnlvuOTJ0/msssuY/369Zx99tlcddVVHDhwoMZ4ly9fTlJSEjabzVc2ZswY9uzZw7Zt2+p833l5eURFRVUpv/3222nbti0nn3wyM2bMwO1WUtYamQO824aanR6c+hmLSAP5q62vb3uvtr4yf7T1GmpeA0tgKPmBEAO4cvb5OxwRaYE8JSVsPuHEZv/eXmt+wqjH9JiRI0dy991343Q6KSkpYe3atZxxxhm4XC5eeuklAFasWEFJSQnJycns2LGj0vnTpk1jzJgxPPDAAwD07NmTZcuWsWDBAgCCgoIIDQ3FYrEQFxdX5fsnTJjAFVdcAcCTTz7J9OnTWblyZaUhYIfKzMwkISGhUllsbKzvWGJi4hHv+ffff2f69Ok8//zzlcoff/xxRo0aRVBQEN9++y333HMP2dnZPPzww0e8prQspvJf5kxuNNRcRBrMX2091K+9V1t/kL/aevV416B/h04UBxoAuJV4i0grlpycTFFREatWrWLp0qX07NmTmJgYkpKSWLVqFUVFRaSmptK5c2e6du1a5fyNGzcybNiwSmWHf67NwIEDfe9DQkIICwsjKysLgH79+hEaGkpoaCjjxo3z1TMMo9I1Kp76H15enT179jB27FguvfRSbrzxxkrHHn74YYYNG8bgwYO55557mDJlCv/617/qfC+txZIlSzjvvPOIj4+v02q0EyZMqHaxmn79+vnqpKSkVFuntLS0ie+mehU93hYX3HOWf+cyiog0NbX1B/mrrVePdw06tYlhhbdNpmy/9vcUkfozgoLoteYnv3xvfXTv3p2OHTuyaNEicnJySEpKAiAuLo7ExER++OEHFi1axJlnnlnt+Ue75aLVaq302TAM35CvL774AofDAeCbfxUXF0dmZmalcyoa74qn4TXZs2cPycnJDBs2jH//+99HjG3o0KHk5+ezd+/eI167NSkqKmLQoEFcd911XHzxxUesP23aNJ5++mnfZ6fTyaBBg7j00ksr1QsPD2fz5s2VygIDAxsn6HoyB3r/PZlc0Kd9uF9iEJGWz19tfcV315Xa+po1V1uvxLsGodZQSsqnFBQdyPJvMCLSIhmGUa8h3/5UsZBKTk4O9913n688KSmJr776ihUrVnDddddVe27fvn1ZsWJFpbLDPwcEBOByueodV5cuXaqUDRs2jAcffBC73U5A+ZZQCxcuJD4+vsqwtEPt3r2b5ORkTjzxRGbNmoXJdORBX2vXriUwMLBO+5S2JuPGjavU63AkERERlVaEnTdvHjk5OVX+zRiGUe0QRH+w2IJwAub6/7MUEfFRW3+Q2vraaah5DZxuD87yxLtEQ81FpJVLTk7m+++/Jy0tzfcUHLyN8RtvvEFpaWm1i60A3HHHHSxYsIBnn32WLVu28PLLL/vmfFVISEggPT2dtLQ0srOzKSsra3CsV155JTabjQkTJrBhwwY+/vhjnnzySSZOnOgbfrZy5Up69+7N7t27Ae/T75EjR9KpUyeee+459u3bR2ZmZqWn6Z9++ilvvPEGGzZs4Pfff+fNN9/koYce4uabb660uIsc2cyZM/nTn/5U5ZepwsJCunTpQseOHTn33HNZu3atnyI82ONtdcHn6zP8FoeISHNRW+/ftl6Jdw1MhoHD6v2hFufl+jcYEZEmlpycTElJCd27d680zCopKYmCggK6detGp06dqj136NChvPnmm0yfPp3BgwezcOHCKguUXHzxxYwdO5bk5GTatWvH7NmzGxxrREQEX3/9Nbt27eKkk07i1ltvZeLEiUycONFXp7i4mM2bN/uGri1cuJDffvuN7777jo4dO9K+fXvfq4LVauXVV19l2LBhDBw4kGnTpjFlypQqi7JI7TIyMvjyyy+rzKnr3bs3KSkpzJ8/n9mzZxMYGMhpp53G1q1ba7xWWVkZ+fn5lV6NJSY8HoBeJU5eWfRbo11XRORYpbbev2294TnaAfvHiPz8fCIiIsjLyyM8vHHmar1y80DOXOKgZFAnTpizsFGuKSKtU2lpKenp6SQmJvptzqr4R00/+6Zol/zBMAw+/vhjLrzwwjrVf+qpp3j++efZs2ePb3hgddxuNyeccAJnnHGGb0Xdwz322GNMnjy5Snlj/J0WpS5gxy13ExDm5JYr3uTriUlHPklEjmtq649ftf3s69req8e7Fic4vfPVQoo1AUxERORIPB4Pb731Ftdcc02tSTeAyWTi5JNPrrXHe9KkSeTl5fleO3fubLRYTeVDzT1u72JwIiIiTaneiffxsMVIBWdQCACeohK/xiEiItISLF68mN9++40bbrjhiHU9Hg9paWmVhgAezmazER4eXunVWJwB3hV2czHjdqmdFxGRplXvxLtii5GXX365TvWnTZtGRkaG77Vz506ioqKq3WLk0HoZGRl+H8LhDA4FwFPS8IUBREREWprCwkLS0tJIS0sD8C2Ws2PHDsDbE33ttddWOW/mzJmceuqp9O/fv8qxyZMn89VXX/HHH3+QlpbGDTfcQFpaGrfcckuT3ktNTDbvKsRul4HLXeyXGERE5PhR7+3EjoctRiqsjywjDigp1ZNwERE5fqxevbrSyrYVi9n85S9/ISUlhYyMDF8SXiEvL4+5c+cybdq0aq+Zm5vLzTffTGZmJhEREQwZMoQlS5ZwyimnNN2N1MISFAZ4VzX3uP07wk5ERFq/Zt/H+0hbjLhcLgYPHszjjz/OkCFDmju8Spwh3qHmtlIPHo/Ht3S9iEhNWsl6lVIPrfFnPnLkyFrvKyUlpUpZREQExcU19xy/+OKLvPjii40RXqMwBXvbeIsLcGtkm4jUXWv8/77UrjF+5s26uFpL2WKkQo8uHQEwecBTyy8TIiJWq3e+aG2Jh7ROFT/zin8D0jIY5fu1Wtxw3+guR6gtIqK2/njWGG19s/Z4p6SkEBkZWWVLkqFDhzJ06FDf59NOO40TTjiB6dOn17jFyFNPPVXtFiONKTGuI06Tt1F2FRRgKu8BFxE5nNlsJjIykqysLACCg4M1SqaV83g8FBcXk5WVRWRkJGaz2d8hST0Y1oOrrg9o1+wDAEWkBVJbf/xpzLa+2Vqapthi5NAN1PPz82vc8L2hIkNi2B0IEcXgys/HeozNQReRY0vFOhUVDbIcHyIjI4+5NUrkyEwBB3stHKVFfoxERFoStfXHp8Zo65st8W7IFiMDBgyosY7NZsNWPkysyRhtKLJ5E+/S3P34d411ETnWGYZB+/btiYmJweFw+DscaQZWq1U93S3VIcMFf9y0i2593VjNzToDT0RaILX1x5/GauvrnXgXFhby22+/+T5XbDESFRVF586dmTRpErt37+add96pdN6RthgZOnQoPXr0ID8/n5deeom0tDReeeWVBtxS48l3hFFSnm3n788k0q/RiEhLYTablYyJHOMMwwCzB1wGX36fx4XnuIgIVuItInWjtl7qq96J9/GwxUiFsPA2mKxuwISroPEXbxMRERH/MZkM3C4Icjuwu9z+DkdERFqxeifex8MWIxXCIqIY6CmjgCBCtZW3iIhIq2JYAAcEuuw4lHiLiEgT0piqWoSFR2K2ehvikn37/ByNiIiINKaiiu4HS5YSbxERaVJKvGthsQVB+QLsZQey/RuMiIiINKri8umZJlOhEm8REWlSSryP4Mdw7+pqW/es93MkIiIi0pjcFu/+uxa3gzKnEm8REWk6SryPoCTQ+zjcU1jg50hERESkMbnKe7wD3E6crprXrxERETlaSryPICjYO9bcVmr3cyQiIiLSmDxmb4/3oLggOkcF+zkaERFpzZR4H0FoqHeouaVEibeIiEhrUjHUPDrAoE1IgJ+jERGR1kyJ9xEEh4YAEFigxFtERKQ1qejxdjscfo5ERERaOyXeR2CJDAMgLN+Nx67kW0REpLXwlPd45+QVsSe3xM/RiIhIa6bE+wjsphDKLN6/qLI9u/0djoiIiDSSHlbvvG7H9gDW7sj1bzAiItKqKfE+gja2aHLDvSudFu3Y5t9gREREpNEEWC0AWN1u7eMtIiJNSon3EQSFRNLVVgaAaW+2n6MRERGRxmJYvPuJBbgd2JV4i4hIE1LifQTWkAgCQlwAOHZrqLmIiEhrsdvsBKDIko/dqcRbRESajhLvIwgOicQa4m2YHbt2+TkaERERaSz7zd4H63ajVEPNRUSkSSnxPoLQiDZ8HWsDYOeWtX6ORkRERBpLxVBzs+Z4i4hIE1PifQS24AgKvTuKYc3K8W8wIiIi0mgMqzfxtrg8OFweP0cjIiKtmRLvIwmKJLh8qHlwbglu7eUtIiLSKlQk3qFmg+ReMX6ORkREWjMl3kcSGkOkxUmp1fvRuWePf+MRERGRRmFYvNuJBXg89I0P93M0IiLSminxPpLQWNq5XWRFeD/ad2llcxERkdbAVL6Pt+HUMHMREWlaSryPxGIjyhTMvggD0JZiIiIirYXJWj6czeFiY0a+f4MREZFWTYl3HZiJICvS+75k5zZ/hiIiIiKNpE9oLABtCyy8vWybf4MREZFWTYl3HQQEtsMU4t3rs0x7eYuIiLQKgYFBAFhdHuzaTkxERJqQEu868ITEcb0r1/shY69fYxEREZHGYQTYADC53didSrxFRKTpKPGuA1NYLAFh3i3F7Nu24/FoERYREZGWbp/JAUCx4cShHm8REWlCSrzrICCyPQGh3sTbnZeHKzfXvwGJiIjIUcszedt2h8eDw6WH6iIi0nSUeNdBcHQ8b0aHk12+xac9Pd2/AYmIiMhRs5QPNbe4PBpqLiIiTUqJdx0ERsbjMWB3lHdLMXv6Nv8GJCIiIkfNYvMurmZ2ocXVRESkSSnxrgMjLI52ThcZUd7P9m3q8RYREWnpLLZA758uuOH0RD9HIyIirZkS77oIjaGdy0VGRY/3tm3+jUdERESOmsW3nRiM6Rfn52hERKQ1U+JdF0FtiPOY2BPt/ajEW0REpOWz2oIBsDj9HIiIiLR6SrzrwjCItrZhT3mPd9n2HXhcLj8HJSIiIkfDGhgCgMUNP6Vn+zkaERFpzZR411GpJ5KyEDd2M2C348jI8HdIIiIichTahEb53t/93mo/RiIiIq2dEu86cgS14wR7GQfaWABtKSYiItLSWYNCD35wOPwXiIiItHpKvOvIExrL9KxsgkIiAW0pJiIi0uIFBPreGvYyPwYiIiKtnRLvOrKEe1c7dZU/HNeWYiIiIi2bYbHiMnsA8DgK/RyNiIi0Zkq86yiwTTwApmDvUDStbC4iItLCmayUWbwLp+JW4i0iIk1HiXcdhbbtyOKgQGZ09zbM9h07/RyRiIhI01iyZAnnnXce8fHxGIbBvHnzaq2fmpqKYRhVXps2bapUb+7cufTt2xebzUbfvn35+OOPm/Au6sBsxWn2vjU5y/B4PP6NR0REWi0l3nUU2C6RYI+HP9p6Pzv27MFjt/s3KBERkSZQVFTEoEGDePnll+t13ubNm8nIyPC9evTo4Tu2fPlyLr/8cq655hrWrVvHNddcw2WXXcaPP/7Y2OHXnclKSYD3bYizBLvL7b9YRESkVat34n3cPAU/XGQX2jud5IZAmRVwu3Hs2ePvqERERBrduHHjeOKJJ7jooovqdV5MTAxxcXG+l9ls9h2bOnUqo0ePZtKkSfTu3ZtJkyYxatQopk6d2sjR14PZQlGQ9+0l3UMwGYb/YhERkVat3on3cfMU/HC2UGIDIjGAzEhvkX3HDn9GJCIickwZMmQI7du3Z9SoUSxatKjSseXLl3PWWWdVKhszZgzLli2r8XplZWXk5+dXejUqk5WS8sT7xEiwmjUQUEREmoalvieMGzeOcePG1fuLYmJiiIyMrPbYoU/BASZNmsTixYuZOnUqs2fPrvd3NRVrmwTaujLIbGPQZZ8H+3Yl3iIiIu3bt+ff//43J554ImVlZfznP/9h1KhRpKamcsYZZwCQmZlJbGxspfNiY2PJzMys8bpPPfUUkydPbrrAzVbKgjyAgePA/qb7HhEROe4126Pdxn4K7g/5QR2Ic7rIbOP9rB5vERER6NWrFzfddBMnnHACw4YN49VXX+Wcc87hueeeq1TPOGwot8fjqVJ2qEmTJpGXl+d77dzZyAubmizYA70Lqh3Ys5dSh6txry8iIlKuyRPviqfgc+fO5aOPPqJXr16MGjWKJUuW+Oo05Cl4kw8/q8bv9mjiXC72tvH+kuBQ4i0iIlKtoUOHsnXrVt/nuLi4Ku16VlZWlfb/UDabjfDw8EqvRmW2cpq7GIDNGwrZk1vSuNcXEREpV++h5vXVq1cvevXq5fs8bNgwdu7cyXPPPecbfgb1fwre5MPPqtOmMwO2lbE1JAwoU4+3iIhIDdauXUv79u19n4cNG8bXX3/N3Xff7StbuHAhw4cP90d4XiYrMRYnGUBEWbFWNRcRkSbT5Il3dYYOHcq7777r+9yQp+CTJk1i4sSJvs/5+fl06tSp8YM9REDbrly3roB0ZzilgH3XLjwuF8Yhq7aKiIi0dIWFhfz222++z+np6aSlpREVFUXnzp2ZNGkSu3fv5p133gG8a7UkJCTQr18/7HY77777LnPnzmXu3Lm+a9x5552cccYZPPPMM1xwwQV88sknfPPNN3z//ffNfn8+ZitmmzfZDrcX43BqH28REWkaflm+s6an4Ic60lPwJh9+Vo3QuK4AxNr2gdUCDgeOjJqHw4uIiLREq1evZsiQIQwZMgSAiRMnMmTIEB599FEAMjIy2HHIqC+73c69997LwIEDGTFiBN9//z2ff/55pe3Ihg8fzgcffMCsWbMYOHAgKSkpzJkzh1NPPbV5b+5QJjObw7x9EOHOXPV4i4hIk6l3j/dx8xS8GpHtu+HyGASb7Jjax+LesRvHzh0EdOzg79BEREQazciRI/F4au79TUlJqfT5/vvv5/777z/idS+55BIuueSSow2vUa2NtHE6EFZWgt2pxFtERJpGvXu8j5un4NUIDwkmk2gui49jlXUPgLYUExERacHMgd71ZELsLhxldj9HIyIirVW9e7yPp6fghzMMgyxzLJHuLPa28c7r1gJrIiIiLZfNZuDG2xPhyssF2td+goiISAP4ZY53S9Y+oTe9yxzsifI+IS87ZKsUERERaVmCTSYKg7zvO5rV4y0iIk1DiXc9xXXuRS+7nd/bexPv0vXrax0BICIiIseuIFs4BeWJdzxl/g1GRERaLSXe9RXVld52O9tiwW4GV14eju3b/R2ViIiINEBIeEcKgr3vXTm5fo1FRERaLyXe9bTL1pUuDicWA7aVbzNesn69f4MSERGRBgmOTKAgyDuKLT9zn5+jERGR1kqJdz2tLozC5bHQw2Fna7y3oS5Zp8RbRESkJerV/mROoASAXzdpwVQREWkaSrzrqWtsG7Z6OnJWUTFt+/QA1OMtIiLSUoXGDSLG4gDAnJ/n52hERKS1UuJdT93ahfKruwvX5RVwUUJvAEo3bcJdpgVZREREWpzo7phs3kVSrXlZfg5GRERaKyXe9RRis7AnsBsAzpLfMLdpAw4HZRs3+jkyERERqS+X2crSNhEA2PL2+jkaERFprZR4N0BpdF8AXAc24uyTCGi4uYiISEtkMkx8Eu1ds8VacMDP0YiISGulxLsBrPEDALg82sNsaxoAhUu/92NEIiIi0hCGYVAaZAbAUlzs52hERKS1UuLdAJ3iO7DL05auDicre3mfkhctX44rN9e/gYmIiEi9lQUHAGAucfg5EhERaa2UeDfAyF7tCO40mB52O3uiDfI7tQGnk4Jvv/V3aCIiIlJPrvBQAEwOjxZLFRGRJqHEuwFiwgOJ6noC3RzeJ+MbBnkXZcn/coE/wxIREZEGCIqOxuUdwIYrc7t/gxERkVZJiXdDxQ+hu92beH/d3TsnrGj5cpw5Of6MSkREROopKDCMEpv3vTvjN/8GIyIirZIS7wZaYe/iS7x/CTqAtXcvcLkoWPi1nyMTERGR+gg0B1NckXhn7fBvMCIi0iop8W6gBdsg392GOKcTgOKkIQDkf/qpH6MSERGR+upju4wQw7uyuTt7l5+jERGR1kiJdwN1jwllnbsb1+YVcF/bobS94CIwDIpXr8a+S422iIhIS9EptDtus7fL23Ug08/RiIhIa6TEu4ES24awzt2Na/ILuLbITnzXAYQMGwZA3ief+Dk6ERERqSur2USxxZt4u3P2+TkaERFpjZR4N1BC2xDWeboB4Nn9EwARF14AQN4n8/F4PH6LTUREROpub9nvZAW5AXDn7fdzNCIi0hop8W6g9uGBbDZ1w+4x2FKSyaIt8wj7058wgoNx7NhBydo0f4coIiIidZBeuJasiHwA3Pm54PHAN5NhxWv+DUxERFoNJd4NZDIZtI1uyzojnks6tOeO5Y9QFmAQlpwMQNEPP/g5QhEREamLYGsIpQHe93tLiljyy/sU//AifP0P/wYmIiKthhLvo5AQHUK6syvhLhcAO/J3EDRoIAClGzf6MzQRERGpoxBLMMXlifciSyC3/fQ0O60WcJWBy+nf4EREpFVQ4n0UHjqnD2NGjiTB4W2U0/PTCezTB1DiLSIi0lLEhUVQYjMAsNq9f+aYyn9FcpX5KywREWlFlHgfhS7RIUTEJpDgcACwLW8btvLE25mRgTMnx5/hiYiISB10iWpDSXmPd5Dd+2eu2buvN04l3iIicvSUeB+t8A4klvd4b8vfhjk0FGuXzgCU/vqrPyMTERGROgi2BlPi3U2MoPI829fjrcRbREQagRLvo1DmdDFjbUmlHm+AwD59vcc13FxEROSYF2gKOqTH27sdaK65IvEu9VNUIiLSmijxPgoBZhOvrSmms728xzsvHY/Hc3Ce969KvEVERI51Fk9bCpyJwMEe71yTd6i5y6EebxEROXoWfwfQkhmGQXx0BMEHQrj7QA4JyXfh9rgJ7FuReGuouYiIyLEuzBZMsTkMODjHO6e8x9vsVuItIiJHT4n3UUpsG8z+/VFcn/cHBLYHk9nX423fvh13URGmkBA/RykiIiI1sZnN2MOyAQgr83Df/hx62MszcM3xFhGRRqCh5kepW7tQMjzR3g/5uwGwtG2LJSYGPB5KNvzix+hERETkSKwWg7ySEwCwOA2uyS3gpJLy/buVeIuISCNQ4n2URvWJJcMTRa7JxFcZP/Ltjm8BCD7pRAAyHn4YR1aWP0MUERGRWljNJgqKhvs+7zfa87snHoB9OXn+CktERFoRJd5HaVDHCEqDYvkx0Ma9uT/y+rrXAYh54AGsnTrh2LmTnTfciLu42M+RioiISHUsJgOnxYKjfEG1VxLvY1mwFYCi4iJ/hiYiIq2EEu+jZBgGHRO6M6jMOxdsS84WSpwlWGNi6PzWTMxt21K2dSv5Xy30c6QiIiJSHcMwCLaaKbYEArAi+0VebO+k1DBwlGk7MREROXpKvBvBmacMIdblIsYNLo+LX/d7VzMP6NSJyIsuAqBo2TJ/higiIiK1uOiEjphDvYuhhtm9Pd+5JhNOe4k/wxIRkVZCiXcjCGnbGQMYUOp9Kv7zvp8PHhvunTNWtHw5Ho/HH+GJiIjIETx+YX+iY6IAaIs3Ac81m3A71OMtIiJHT4l3YwjzLsAysNT7VHx99nrfoaAThmAEBeHKzqZsyxa/hCciIiJHVrH9pyPHALw93i67Em8RETl6SrwbgyUAR1A7BpbP8163b53vkCkggOCTTwKg6AcNNxcRETlWuYOCAQgosQCQazarx1tERBqFEu9GYkR0oG+ZHTwGWcVZZBZl+o75hptrnreIiMgxKW1nLl+lFwAQVOpd0TzHZMLjVOItIiJHr96J95IlSzjvvPOIj4/HMAzmzZtXa/2PPvqI0aNH065dO8LDwxk2bBhfffVVpTopKSkYhlHlVVracho7S2RHgj0eTt0zkIcGvENscKzvWEXiXbxqFe6yMn+FKCIiIjVIiA6mxGIDILDM++tRrtnEkPhgf4YlIiKtRL0T76KiIgYNGsTLL79cp/pLlixh9OjRfPHFF/z0008kJydz3nnnsXbt2kr1wsPDycjIqPQKDAysb3j+0yYBgKTiEnZkBWEYhu+QrUcPLO3a4Skro+Sw+xYRERH/iwwOwFU+1DywMIrxpYmMKC4Fpx6Yi4jI0at34j1u3DieeOIJLirfJutIpk6dyv3338/JJ59Mjx49ePLJJ+nRoweffvpppXqGYRAXF1fp1aJ08fZqDzf9wpodOQC+VcwNwyD41FMBKPrxR//EJyIiUkfH6+i2oIgwAAJLghlj6k5/ux001FxERBpBs8/xdrvdFBQUEBUVVam8sLCQLl260LFjR84999wqPeKHKysrIz8/v9LLr7qchscw0c2UwY7s77jhqxt459d3fIeDTz0FgOKVq/wVoYiISJ0cr6PbQtpEABDsLGVngRuArXv2+zMkERFpJSzN/YXPP/88RUVFXHbZZb6y3r17k5KSwoABA8jPz2fatGmcdtpprFu3jh49elR7naeeeorJkyc3V9hHFhQJ7QfBnrV0MP/Kysz1ZBVncW3fazEMg5DyHu+S9etxl5RgCgryb7wiIiI1GDduHOPGjatz/alTp1b6/OSTT/LJJ5/w6aefMmTIEF95xei2Y1VE20gAgp3FpJsLWR1owygs9G9QIiLSKjRrj/fs2bN57LHHmDNnDjExMb7yoUOHcvXVVzNo0CBGjBjBhx9+SM+ePZk+fXqN15o0aRJ5eXm+186dO5vjFmplJJ4BwN3BTgLNQWzL38baLO/TfmunTljatweHg+I1a/wZpoiISJNqqaPbotu18f5pLuH9yB+Z1C4ak0tzvEVE5Og1W+I9Z84cbrjhBj788EP+9Kc/1VrXZDJx8skns3Xr1hrr2Gw2wsPDK738LjEJgOHOXzg7cSwAc7fOBbxP+UNOKR9u/uNK/8QnIiLSDGob3TZ//nxmz55NYGAgp512Wq1t/VNPPUVERITv1alTpyaNu1PHtgDEmg2i8zx4SgwMlx2AFRkrKm0VKiIiUh/NknjPnj2bCRMm8P7773POOeccsb7H4yEtLY327ds3Q3SNqPNQMFkhbycXxQ0DYOG2heTbvU/oKxZYK9YCayIi0kq15NFtce2jAQjP2c+Lb7h45D8enM4SVmas5KaFN3HW/85q0u8XEZHWq96Jd2FhIWlpaaSlpQGQnp5OWloaO3bsALyN5LXXXuurP3v2bK699lqef/55hg4dSmZmJpmZmeTl5fnqTJ48ma+++oo//viDtLQ0brjhBtLS0rjllluO8vaaWUAIno4nARC/cwfdI7tT6irlyz++BCC4vMe7ZMMGXJozJiIirUxLH91mCgkFwFNQSKADYvLAvr+YjKIMAKICo2o7XUREpEb1TrxXr17NkCFDfIulTJw4kSFDhvDoo48CkJGR4UvCAV5//XWcTie33XYb7du3973uvPNOX53c3Fxuvvlm+vTpw1lnncXu3btZsmQJp5Qnqi1JZpQ35rQl87moh3fLtYrh5gEdOxCQkAAuFwWHbbMiIiLSkrWG0W3m0JCqhRll2N3e4eYD2g1o5ohERKS1qPeq5iNHjvTtT12dlJSUSp9TU1OPeM0XX3yRF198sb6hHJNsPc+EtGkMca2HLu+weOdiLuh+AW6PG5NhIuLii9j3/AvkfPghkRdf7O9wRUREqigsLOS3337zfa4Y3RYVFUXnzp2ZNGkSu3fv5p13vNtmVoxumzZtmm90G0BQUBAREd4tuiZPnszQoUPp0aMH+fn5vPTSS6SlpfHKK680/w3WwBRyMPHObmOmbY6LgAwnOaU5ALSxtfFXaCIi0sI1+z7erV1kj2GUeAJoa+TjztzBm2Pe5Lxu52EyvH/VkePHg8VC6br1lG7e4udoRUREqjpeR7eZwsMJHDAAW48efHZNFwCCsyArezsAH//2sT/DExGRFqzZ9/Fu7UxWGz+b+3KKO43Szd9BtyGVjlvatiUsOZmCr78m97//Je7hh/wUqYiISPWO19FthslEwodzwOViZNoMysKmYyswYfl5C0R667jcLswms1/jFBGRlkc93k1ga8gJAFh3fI/H42Fb3jY+3voxbo8bgMjy7VXy5s3Tnt4iIiLHEMMwMCwWxnVKJrZdCQDxv+zzHS/Tvt4iItIASrybwN5o77C5qH0rcbnsXP7Z5Ty67FG25nhXbg05bTiB/frhLixk+zXXcuCd//gzXBERETlMqcdKSJw3ye7xR6mvXIm3iIg0hBLvJuCOG0i+JxibqxDL3g0MifEON1+9dzXgHcrW+e23CT/nHHC52PvUU9gPmSsnIiIi/pXntvNHrPd9mwMOLCbv7Dwl3iIi0hBKvJvAyV1j2BXp3c+bzQs4MfZEAH7a+5Ovjjk0hPjn/kXIaaeBx0POB3P8EaqIiIhU48ecdfw1oR0AnuJiQj02AEqdpbWdJiIiUi0l3k0gqWc7+o661vvhl485McY75/unvT9VWqzGMAzaXHUVAHlz5+IuVWMuIiJyLGgT3JZiG7gMb7sdVWYF1OMtIiINo8S7qfQaC5ZA2L+V/m4Dm9nGgdIDbM7ZXKlaaNIZWOPjceXlkf/5F34KVkRERA7VJrgdGAYFQQYAjlzvXt6lLj0kFxGR+lPi3URyXTbyOyYDELDxM87oeAYAM3+eWameYTbT5sorAMh5771at28RERGR5hEZ5B1mXhjk/Rxe4uGDcz4gIT8Qt93ux8hERKQlUuLdRM54dhEPbunu/bDhI/464GYA1mStochRVKluxMUXYwQEUPrrr5SuW9fcoYqIiMhhIgPbAFBQnnj3MsXTNRP2nHMhGZMe9GNkIiLSEinxbiLxkUF85x6CyxwIOen0spcx/czpfD7+c0KsIZXqWtq0IfzsswE48P77/ghXREREDhFiDcHi8VBYPtQ82mGjdOOvANjT0/0ZmoiItEBKvJtIt3ahFBPItlDvVmLsWs3ITiMJtARWW7/NVVcCUPDlApz79zdXmCIiIlINwzBo44b8YO/n3L07WPnzAgAthioiIvWmxLuJnDuwPQDfF5RvApr1q++Yx+Oh2FFcqX7QgAEEDhiAx+Eg939zmy1OERERqd6NpQalgd61V4KKnGzauhwAd2mJP8MSEZEWSIl3EzmzTwwRQVbWlMZ7C/b+AsCSXUsYO3csk5ZOqnJORa93zvvva+EWERERP7vSFUBHkwOAsBJoU+gtdxeXUFTm1IKoIiJSZ0q8m4jNYub8QfFs8nT2Fuz9FTweogOj2VO0h5WZK3G4HZXOCT/7bCyxsTj37iVvrnq9RURE/CnXYSLA5ga8iXdkoTfRdhQV0+8fXzHpo5/9GZ6IiLQgSryb0MUnduQPTzwOjxnK8iBvF72jehNpi6TQUciG7A2V6psCAoi++SYAsl//t3q9RURE/Gi7y6CbUQZAF08Ubco3JTE77BgeNxHBVj9GJyIiLYkS7yY0qGMEj1wwCNr19Bbs/QWzyczQ9kMBWLZnWZVzIi+5xNvrnZlJ7v/+15zhioiIyCE+DfHweAfvtmKBhXYiCw8eC3A5+GTtHj9FJiIiLY0S7yZkGAbXDkvA2n6At2Cvt4d7ePxwoPrE22Sz+Xq9s557npK0tGaJVURERCoLw0ph+T7eQdmFWNwHjwW6HGTma3VzERGpGyXezSG2n/fP8gXWhsUPA2BD9gb2Fe+rUr3NpZcSMnwYnuJidtz8V0p//bVKHREREWlaIQT4thMzuSsvpGZzaTqYiIjUnRLvZrC8yLulWMlu7yIscSFxDG43GLfHzQebP6hS3wgIoOPLLxN0wgm48/PZfvU1FHz7bbPGLCIicrwzzFaKAqs/FuhU4i0iInWnxLsZLNzXFgBb7h/g8A5Lu67/dVzc42LOSTyn2nNMwcF0en0GwUOH4i4uZtdtt5M7b15zhSwiInLcCzbZcJsMCqtJvm0uB1az0fxBiYhIi6TEuxmcOKAvOZ5QTLgg7T0Azux8Jo8Nf4yukV1rPM8cFkbnN/5N5KWXArD/9X9rz1AREZFmcpKpLWcVFuGyVV293OayE2qz+CEqERFpiZR4N4MzesXwjmsMAJ7P74Gf675auWG1EvP3+zECArCnp1O2ZWtThSkiIiKHSGgXxfP79hMTGV7lWKDLjh6Fi4hIXSnxbgbhgVYWt7+ed52jMPDAx7dAYRYAv+X8xj2p97Bg24IazzeHhhIyYgQA+Qu+bJaYRUREjndWm3dJc0tINT3eTgd2p7tKuYiISHWUeDeT/h0jecR5HfttncDtgIz1AHy9/WsWbl/IjLQZuNyuGs8PHzsWgIIvF2i4uYiISHOw2Lx/BFcdUv7o6K4suPOM5o5IRERaKCXezaR/fAQeTPxmdPEWZG8G4Oq+VxMWEMbveb+zcPvCGs8PTU72Djffto2yzZubI2QREZHjWr7TDMABp9NXVhDs/dXpu7TtdI4O9ktcIiLS8ijxbib9OoQTYDaRYe3sLdjnTZ7DAsK4tu+1ALya9iplrrJqzzeHhhCa5H2ynv3KK3js2sZERESkKRW7vT3duW6HryyrjTcZDzNqHqUmIiJyOCXezaRPXDi/TBnDhaOTvQXZW3zHru5zNdGB0WzL38araa/WeI02V18DZjMFX3/Dzv+7FWdOTlOHLSIictwKsHn3EfNYD07xyoz0biG24Y8snv5yE6UOJeAiInJkSrybiclkYDWboG1Pb8EhiXdoQCiPDnsUgJRfUli3b1211wg59RQ6vfYqRlAQRT/8wG+j/sTep5/BXVra5PGLiIgcbwJs5UPJA7yLqBUHQF6wNwkPdNqZsfh3isqcNZ0uIiLio8S7ubXt4f2zeD8U7fcVn9n5TM7peg5uj5tnVz5b4+mhZ5xBl3fewda3D57iYg6kpJD1wgtNHbWIiMhxxxbkTbxNQd5ke18ElFq8Pdw2l3fKl8OlBU9FROTIlHg3o+W/72fca2vINsd6C7IrL5J2/8n381zSc7w++vVarxM0oD+Jc+cS/8zTAOTO/gDHnj1NErOIiMjxyhrgHWpuigLr3+4k7KF7MVynAmBzeed9a0sxERGpCyXezSg4wMzGjHy2uNp7Cw4Zbg4QFRjFmIQxhAaEHvFahmEQfv75BJ96Kh6Hg32vvNIUIYuIiBy/LN7E22Zy4L7kKk4bewN5Du/D88DyHm+7S3O8RUTkyJR4N6NecWFYTAYbneWJ974ttZ9wBIZhEHP3XQDkfTyPsj/SjzJCERER8Snfx9uGg4JSJ263h15d2gEQ7HZgjVzBP1bexbzf5vkxSBERaQmUeDejQKuZUxKj+M0T7y3Irroft9PtZMa6GVz1xVUU2AuOeM2gwYMJOWMEuN3kf/lFY4csIiJy/DJ7E++ebQMY2DGCBdu+pGOid32WUJyYAjNYv38lewo13UtERGqnxLuZXXRCR35zdwDAs69q4m0xWfgi/QvW71vPD3t+qNM1w84cBUDxih8bL1AREZHjXflQc6vbjtVs4p8//pMPts8DvKuam4N2APDautf8FaGIiLQQSryb2bj+cey2dgLAyNsJ25dXqTOy40gA3vr5LRZuW4ijfAGXmoQM9S70UpKWhrukpHEDFhEROV6VDzXH6d2202a2Ybd6i7qFW2gbUXv7LCIiUkGJdzMLsVkY2r8nqa5B3oL/XAibKg8RH5MwBoCNBzZyz+J7eGTZI7Ve09qlC5b27fE4HBSvWdMUYYuIiBx/bGEA2IvzWLp1Hy6XhTKrAUCA006A2eyr6nRrP28REalZvRPvJUuWcN555xEfH49hGMybN++I5yxevJgTTzyRwMBAunbtyowZM6rUmTt3Ln379sVms9G3b18+/vjj+obWYlx2Uic+7f0MBzqc6X2K/r/rofiA73i/tv3473n/5eo+VwPw7fZvKXHW3JNtGAYhp3p7vTXcXEREpJEEtQEgwF1KWnomhsdKWXmPt7ukhPaZdp6d6eTErW7yyvL8GKiIiBzr6p14FxUVMWjQIF5++eU61U9PT+fss89mxIgRrF27lgcffJA77riDuXPn+uosX76cyy+/nGuuuYZ169ZxzTXXcNlll/Hjj60ziRzaNZrnrxpG1PX/hZh+4CyBn/9bqU7vqN7cf/L9xIfEU+oqZfmeqkPSDxVcPty8qJX+nYmIiDQ7Wzju8l+VXIUHgIOJd1lhMb3W55GQBaf/4lHiLSIitbLU94Rx48Yxbty4OtefMWMGnTt3ZurUqQD06dOH1atX89xzz3HxxRcDMHXqVEaPHs2kSZMAmDRpEosXL2bq1KnMnj27viG2HGYLnPgX+PJ+WPMOnHIzGIbvsGEYTOg/AbvLTt/ovrVeqqLHu3TDBlwFBZjDwpo0dBERkVbPZKLMEk6QMxdPSQ54rJSV/+bkKS0hLN/7PrgMcspy/BeniIgc85p8jvfy5cs566yzKpWNGTOG1atX43A4aq2zbNmyGq9bVlZGfn5+pVdL4vF4+HVPPi/tG4zHbIO9G2DP2ir1ruh9BX/p9xfiQuJqvZ61fXsCunQBt5s9D0zCmaNfAERERI6WPSDC+6YkB7fb4uvxtricRGZ2ByCk1ENuaa5/AhQRkRahyRPvzMxMYmNjK5XFxsbidDrJzs6utU5mZmaN133qqaeIiIjwvTp16tT4wTex295fwwvfZ7O7/Z+8Bd9OhiXPQcb6Bl2v3V13gtVK4bff8sd557P3mWcp3bylESMWERE5vjgDIgEwl+US5RiHsfd837H2Rd49vUPsJspcZf4IT0REWohmWdXcOGT4NHh7ew8vr67O4WWHmjRpEnl5eb7Xzp07GzHipmcYBucNbA/AK7nDvIV/pMJ3j8N//1Kpbl5ZHp/89gkfb619wbnwceNI+GA2AQkJuLKzOTBrFunjx1Pw3aKmuAUREZFWzxUYCYClLJeOuR5+8LziOxZX5F0YtYsRzdldz8aZnc2B997DVVDgj1BFROQY1uSJd1xcXJWe66ysLCwWC9HR0bXWObwX/FA2m43w8PBKr5bm/MHxAMzOTuQ5x6V85jnNe+DAH1B6cJGWdfvW8fAPD/PiTy9id9lrvWZQv34kzv+EDtNfIuS008DtZvc991Dy84Ymuw8REZFWq2Jlc3se57Tbi2GAUT7P2+pxAfgS7f1vzmTv40+Q++GHfglVRESOXU2eeA8bNoyvv/66UtnChQs56aSTsFqttdYZPnx4U4fnV91jwnhy/ACuPLUL71gv5fay2ygL9vaCk7XJV294/HBig2PJKcvhm+3fHPG6poAAwkePptOM1wg5/XQ8JSXsuOEG9k1/WXO/RUTkiLR16EFtor2dAFcMCCUxLofPQoJxWzyV6nhKS/HY7TgyMgBwZNQ8VU5ERI5P9U68CwsLSUtLIy0tDfBuF5aWlsaOHTsA7xDwa6+91lf/lltuYfv27UycOJGNGzfy1ltvMXPmTO69915fnTvvvJOFCxfyzDPPsGnTJp555hm++eYb7rrrrqO7uxbgylM78+T4AYzo2Q6AfUFdvQeyfvXVsZgsXNzDuwL8f7f8t8o1amJYrXSY+iKBAwbgzs8n+5VX+OP883Hs3dt4NyAiIq2Otg49yBrWFoAQdwGf5f7MpJi2lFg9Veq9tPRpXLm5ALj0kFtERA5T78R79erVDBkyhCFDhgAwceJEhgwZwqOPPgpARkaGLwkHSExM5IsvviA1NZXBgwfz+OOP89JLL/m2EgMYPnw4H3zwAbNmzWLgwIGkpKQwZ84cTi3fIut4cN9ZvfjxwVF07HWCtyBrY6Xj43uMx2SYWL13NX/k/VHn65pDQ0mY/T4dXnyBgC5dcO3LZs899+JxOhszfBERaUXGjRvHE088wUUXXVSn+oduHdqnTx9uvPFGrr/+ep577jlfnUO3Du3duzeTJk1i1KhRvu1Gj1nlQ83dRQewlpUA4KxmM9atu9bhyvNOE6tIwEVERCrUex/vkSNH+hZHq05KSkqVsqSkJNasWVPrdS+55BIuueSS+obTaiS0DfG+iSnfr/uQHm+AuJA4zuh4Bqk7U3lgyQM8PeJpukZ2rdO1DYuF8HHjCOzTh/SLLqZ49WqyX32Vdnfc0Yh3ICIix6uatgWdOXMmDocDq9XK8uXLufvuu6vUqS3xLisro6zs4Grh/tg61GWLwAxsSt9Bji0PosFhrVrPkZeHK7d8zrd6vEVE5DDNsqq51ENMH++fh/V4A/zfoP8jLCCMjQc28vr61+t96YCEBOKmTAEg+99vYN+1+6hCFRERgda9dagpOMr7piSHKI+3x9tRTbeFsyDv4FBz9XiLiMhhlHgfQ77duJcbPs/HgwHF2VC4r9LxvtF9+fj8jzmn6zn8/ZS/N+g7Is49h5Dhw8Dp5MBbbzVG2CIiIq1261CjPPGONAqILk+87dX0eNtyi/GUlgJKvEVEpCol3seQfQVlfPt7IZnm8pXN174Dz/eBpc/76sSGxPL0iKeJCoxq8PdE3/xXAHL/9z+c+7zJvcflInfuR5T9kd7wGxARkeNSq946tHyOdyw5hOJdH6WsmsQ79pDR5e7iYtz22rf/FBGR44sS72PI6T28K6eut3v39+bbKVCwB9a8U219h9vBT3t/qvf3BJ96CkGDBuGx29lfPid//5szyXjoIXb97W+1zuEXERE5XKveOrQ88TYbHmzl7eOhibc9IBCA2NzKbacrJ7dZwhMRkZZBifcxpGObYE5OaMMWT8fKB3K2QVF2paJCeyFnf3Q21391PTvz6zf0zjAMom/x9nofePsdcj6YQ/arrwJg//13in9c2eB7EBGRlk9bhx4iMAJX+a9L3e0OHth/gMHOUt/h/dHeh+XxeZV/pXLlaoE1ERE5SIn3MebCIR3Y4q5IvA0IjPS+3V25Zzs0IJSuEV1xe9zc8s0t7MjfQX2EjhxJ+LnngtNJ5mOP4SkrA7MZgJzZs4/yLkREpCXT1qGHMAyKTGEAdHc4uCq/kHCT23d4X3ni3bu0TaXT1OMtIiKHUuJ9jDlnQHuWGCfytetEMkc8Ab3O9h7YtbpK3YdOfYgOoR3YUbCDq764ii05W+r8PYZhEP/kPwkpH+JnWK10eN47l7zg229xZGUd/c2IiEiLVLF16OGvii1DU1JSSE1NrXROxdahZWVlpKenc8stt1S57iWXXMKmTZuw2+1s3LixzvuE+1uJ+eDc8tKgGOzlq5q7gazIGABc+yqPTFOPt4iIHEqJ9zEmMjiAU3t15ibHPXwWcDZ0PNF7YHfVxLtzeGfePftd+kX3I7csl799+zcOlB6o83cZAQF0eOklom64ng7TphI+dgxBJ5wATie5cz5srFsSERFp0SKjY3zvXR0GsCosAID8EIPCgNBqz9HK5iIicigl3segi0/sSFRIAFcP7QIdTvIW7v4J3O4qddsGteX10a/TOawze4r2cMd3d+BwO+r8XebQEGLvu4+wM88EoM1VVwKw/623tM+3iIgIYAtr63tfFtOHFaHeBdVyQqDQEljtOa4c9XiLiMhBSryPQWf1jeXt604h0GqG2H5gCYTSPDjwe7X1I2wRTB81nVBrKE63E4thafB3h48bR9BJJ+IpKSHzH//QCuciIiJBB+dvR8UOoCDI+35fJPz5zL6V65avl6IebxEROZQS72OQYRgM6BiBw+Um9bcc9ob08h5Y+jx8chtkbapyTteIrswYPYM7htyBYRgN/26TifZTHscICKDohx848NZbeDweilb8SMYjj1C8Zm2Dry0iItISZbuDfe9zzFFYE+H9JBMfjrTQPTGuUt2Ajt4FUpV4i4jIoZR4H8MM4KZ3VvPp/vJ9vdfNhrXvwsKHqq0/qN0ghnfwLpbmcDlYsmtJg3qsbV0Tafu32wHI+tdzpI+/iB0TJpD73/+x/cor2fPgQ7iLihp0TyIiIi3NtmKb731wVAeesLTFNKCYx0f8FVNYeKW6AYmJADg11FxERA6hxPsYZjGb6No2lE9cp+G0BEO73t4Df6RCcc2LqDncDs75+Bxu+/Y2NmRvaNB3R994IzF//ztYrZRt8vawB53kXegt76OPyJg8uUHXFRERaWmKD1nV3BbZnvDQOCZnH8C5vYzP0wsq1Q1ISADAlZvXnCGKiMgxTon3Ma57bCg/e7oya8T3cNuPEDcA3E7YOL/Gc6wmKyfFehdl+3BLw1YnNwyD6OsmkDB7NpGXX07nd94m4d136fTmm2AykT//U/K//LJB1xYREWlJisv38S7zWL3zvUPaAbB07S88vWRn5cqdvaPUtLiaiIgcSon3Ma5HjHebkq37Cr0F/cr3PN0wt9bzLut1GQBfpn9JXlnDn7oH9e9H+8mPEXLKKQCEnn4a0X+9GYCMxyZrv28REWn1Tu7XHYACaxQYBoTGAtDWyKPIY8YICPDVLYz19o5rjreIiBxKifcxrkeM9yn7lr3liXf/8sR72/dQWHPSO6jdILpFdKPMVcaSXUsaNaZ2t96KrW8f3Hl55H08r1GvLSIicqyJ7jUCV6fhRCd71z8h1LuvdzsjD4fLgykszFe3uH0kAO6CAjyOum/vKSIirZsS72Ncj1hvj/dvWYXehdLaJECHE8Hjhl8+rvE8wzA4s7N3b+5FOxc1akyG1UqbP/8ZgIJvvmnUa4uIiBxzbKGYb/gS47Q7vJ+DogAIpwi7y425PPE2goPp3+t0b6844MrTPG8REfFS4n2MS4gOwWIyKCxzkplf6i0c4B1GzupZUMuq5RWJ9w+7f8DuslPiLKHMVdYocYWdeSYYBqU//4wjM7NRrikiItIi2LyJdqhRAoBRnnibIyMwzGbM4eXDzWuY533gP++y5+GHse/a1QzBiojIsUCJ9zEuwGLi5StPYP7tp+H2wGupv/NrzNlgDYZ9G71DzmvQN7ov7YLaUews5qttX3H+vPO5YN4F2F32o47L0rYtQUOGAFDw7beU/LyBvPnzG7R9mYiISItSnniH4U28CfGOTjNHRlb6s7p53h6Ph6wXXyTvf3P545xzOfDue00drYiIHAOUeLcAY/vH4fbAha/8wDMLNnH2v39mcaC3N5tVb9R4nskwMeW0Kcw9fy7/3fJfMosy2V24m5/2/tQocYWNGgXAgZS3vft73/93ipcvb5Rri4iIHLMO6/Em1Pt5qyuDF356wZd4O6tJvN2FhXiKiwHwlJWx98kncR6oeYtQERFpHZR4txDRIQHYnW5iw22YDHgy+3TvgY2fQd7uGs87vcPp9Ijswbldz/WVfb+75l7y+gj7kzfxduzc6VtAJu/Tzxrl2iIiIscsm7eHO9pi560JJxEQ6R1avtOUx/p96zG3aQNA7gdzKPvjj0qnOvftA8AUEoIlNhbcbhwaci4i0uop8W4hOkUF8+4Np7Lo3pH847x+bPZ0ZpNtAHhc8M75kLmhxnMNw+CyXpcxZfgUoPES74AuXbD16QNA0KBBABQsXIi7tJSSDb9QtHJlo3yPiIjIMcXmTbQtrmLO7NmWgMgIAAqCYF/xPkKTzgCg6IcfSL/gQko3b/ad6tyX7T23XTus8d49vx17MpozehER8QMl3i3IgI4RBAdY6BfvbfD/6b4OwuJh/2/wxpmw6EmwF9V4/pmdz8RkmPgj7w/2FO5plJg6vPA88c8+Q5d3/4M1Ph53URFZL7zAtj//mR3X/oUD77+P88AB9j79jOaxiYhI62A7uH0Y9kLCzz8f82mnsGiQiX3Fe4m87FISP/4IW98+eBwOChZ+7ate0eNdOfFunDZZRESOXUq8W6CKvb1/KIyj6PpU6HEWuMpg8TPw8ilQsLfa8yJsEQxq5+2Zbqxeb1tiIhHnn49htRJ+zjkA5LzzH3A6Adg75XF+HzuOAykp7H3iCSXfIiLS8llsYA4A4LNVmynpkEDHGa+RHmdQ4iqj6KeZBPbpQ+RFFwNQsuFn36nO7EMT7/YAODLU4y0i0top8W6BIoKtLLhrBL9OGUtIm1i48kO47B1v73f+Lvj5wxrP/b9B/8fro1/ngu4XNHpc4ecdnEdu69WLNlddBYA7Px9Lu3YA7H3ySQpSUxv9u0VERJpVea/3tC/WsjOnmGBrMGGYAcja7n24HTRwAACl63/27fpxsMe7LZb2FYm3erxFRFo7i78DkIbpHRd+8INhQN8LIHcHLHzYu8XY8L9Ve96w+GFNFlNgz56EJJ1B2datdHx5OtaOHQlISACPhzZ/vpyMKVPI+99cMh95lNBF32FY9M9PRERaKFsYFO8njGIcLjcAbT0GBQZkH9hKV8DWuzdYrbhycnDs3k1Ax464sg+Z412eeDs1x1tEpNVT5tOaJIzw/rl9GbicYK79x7tx/0Y2HthIyi8pnBBzAo8MfQSzyXxUIXR+/XU8bjeGyTuYIuqaq33H2j/6KIXfLcK5bx9FP/xAaFLSUX2XiIiI3xyypViZ05t4x7hc7DdcFBfsApcDU0AAgb16UbphA6Xr1xPQsaOvx9vctu3BOd4aai4i0uppqHkLtTmzgAfmrueJz349WBg3AAIjoCwfMtfVev7HWz/mss8u4x/L/kF6Xjpzt87l8RWP+4bCHY2KpLtKeUAAEeXD0XM/nnfU3yMiIuI35Subh1KCvTzxfn3vAX7YsZuRhQWw/3fg4HDzkp+9u49Ut7iaKycHd0lJs4YvIiLNS4l3C1VY5uCDVTv5/OdDnpKbzNDlNO/7bbUvnhYfGo/ZMGMz2xjffTwmw8TcrXP51+p/4fa4myzuiPHjASj89ltcubkAOHNy2PvU05T++mstZ4qIiBxDDunxdrg8YC/CbC84eDzrFwAC+vcHoOTn9UDl7cTMYWGYQr17gqvXW0SkdVPi3UJ1L1/ZPCOvlGcWbGLs1CXsLyw7ONw8fWmt55/a/lTmXzifBRcvYMppU3hk6CMAzNs6j4yipmv8A3v3xtbHu71K3hdf4PF4yHhgEgfefpvMyVOa7HtFREQaVXniHVbR412YVfn43l95ac1LXLfjnwCU/vIr7pIS30PnikVHK+Z5ay9vEZHWTXO8W6iIICux4Tb25pfxWurv3HtWT9oEB0DC6d4KO5aDywFma43X6Bze2ff+kp6XYDVZiQ+Np0NohyaNPXL8hezduJHs6S9j37aNwsWLAShZtw77jh0EdO58hCuIiIj4WYC3pzqUEu/iaoVZ5JkMHmzXlh0WC/P2/sIbu37BiPRgDzQTUFJC0YoV3nOtVswREQBY4ttTtnWrVjYXEWnl1OPdgvWMDfO9D7VZMJkMiO0PQW3AXgivDoVlL4O7bkPHL+h+ASfHndxU4fpEXHQRtj59cOXkePf8BkzBwQDkffYZRSt+ZNuVV1GSltbksYiIiDRIeY/3+X3COSmhDRTuJdTtYVWgjW0BVn7K8U6f8hgGZd29w80LFiwAwBId7VsPxbeyuYaai4i0akq8W7Cz+sYSYDHx6Ll9mXBaIgCZBXYyBv0NrMGw/zdY+BAseADqsWia0+1kya4lLNuzrEniNoeGkvD+e0RccD4AQSecQOxDDwGQN/cjdt91FyVr1pD5+BONstibiIhIoytfXK1buJuObYKhKAsz0N2wAfA/CgFwlcaxKqonAPlffwMcHGYOYI33jjLTUHMRkdZNiXcLds2wBDY8NobrT/cm3V/8nMGwp7/llt+Hwr1b4CzvvDJWvg6pT9X5unM2z+G2b2/jlbWvNEXYAJiCgmj/9NMkfjKPzrPeImzMGIzAQBy7d/vmv5X+8gtF39e+SJyIiIhflPd4U1a+oFr5HO+egd6k+svQEABcJV1I79wPAE9xMQCWtm19l/HN8VaPt4hIq6bEu4ULsBz8EZ6U0AYDWLczl20FJhh+O5z9nPfg4mdg46d1uubYhLFYDAvrs9ezdNdSVmSs4JmVz3DDVzew6cCmRovdMAwCe/XCZLNhDg0h7MxkAEwhIYSNGQNA9mszfL3e2f9+gz/OvwD7jh2NFoOIiEiDlCfeWdnZrNmRwy9btgLQIzi+UjVXSSd+7/YDBUEHyyr3eFcsrqY53iIirZkS71YkJiyQ07p7n6J/kraHb37dy39NY/EM+5u3wvy/Qd7uI14nOiiapE5JANz67a3ctPAm3t34LsHWYBLCE5oqfKL/egtBJ5xA/PPPEfvggxhWKyVr1lC4KJXin35i34svUrZlC/umTmuyGEREROqkPPHenpHJRa8uY8+u7QD0jEioVM1V0oWQsGI2dDF8ZZUS74oe78xMPA5HEwctIiL+osS7lblwsHeu2CuLfuORTzaQVVBGWdJDED8ESnLg47/Wab73Df1vIDY4lqjAKDqEdmBMwhhuHXQrgZbAJos9sFdPEt5/j7CRI7HGxhB52WUA7L7rLnZPvMcXd/6XX1K2dWuTxSEiInJEFft4UwJAOyMXgB5tevuqXEMP7hk5nL7Rvfg54dDE++BQc0tcnHeFc4eD0l9+aYbARUTEHxqUeL/66qskJiYSGBjIiSeeyNKlNe8ZPWHCBAzDqPLq16+fr05KSkq1dUpLSxsS3nFtTP84Aq0m7C43+wvt9G0fTmBgEFw807vg2ral8Pu3R7zOgHYD+ObSb1h8+WIWXLyA55Keo090HwA8Hg9vbXiLvUV7m/ReYu6/j9BRo/DY7Tj37sUSG0vI6aeDx8O+V19t0u8WERGpVfniamFGCd3ahdDWyAMgsk1XEq3hDCot44qItvw1qTs92nQ/LPE+2ONtmEwEn3IKAEUrfmzGGxARkeZU78R7zpw53HXXXTz00EOsXbuWESNGMG7cOHbUMO922rRpZGRk+F47d+4kKiqKSy+9tFK98PDwSvUyMjIIDGy63tXWKtRmYdK4Pozo0ZY5fx1Kcu8Y74HobnDidd733089qu+YsW4GL/70In/9+q8U2AuOLuBamGw2Ok59kchLL8EcEUH8U08Sc9+9ABQs+IrSX39tsu8WERGp1SE93t3ahtAOb+LtCmnHJ12v4d2MvZj25dHjoS95/3sHeyNhT1sTGAYBiYmVLhU89FQAin5c0ay3ICIizafeifcLL7zADTfcwI033kifPn2YOnUqnTp14rXXXqu2fkREBHFxcb7X6tWrycnJ4brrrqtUzzCMSvXi4uIadkfCX4Yn8J8bTmVI5zaU2F18krabH//YD8NuA5PV2+u9c1WDr39+9/OJCY7h97zf+e+W/zZi5FUZVivtH3+cHsuXETJ8OIG9ehF+zjng8ZAxeTKeOu5RLiIi0qhsoYA38e4QZMdmeOdn73WHYwS1AcBeeAAAR2k7zCYLT15iEDbjBWxdu1a6VMjQoQCUrFmLu6ysue5ARESaUb0Sb7vdzk8//cRZZ51Vqfyss85i2bK67fk8c+ZM/vSnP9GlS5dK5YWFhXTp0oWOHTty7rnnsnbt2lqvU1ZWRn5+fqWXVPVa6m/c+UEa/17yB0R0wNnfO9Jg+ydTGnzNDqEduGXQLQB88ccX1dZp7P23DdPBf6ox99+PKSSE0nXrOfD2OxStXEnp5i2Vv9/tZt8rr7DtiivZfMqpZD7+RKPGIyIix7nyHm+L4Wbl2jQA8j3BbM/zQGCE93NONgBFpQadwzuT1cZge/ewKpcK6NoVc7u2eMrKKElb1zzxi4hIs6pX4p2dnY3L5SI2NrZSeWxsLJmZmUc8PyMjgy+//JIbb7yxUnnv3r1JSUlh/vz5zJ49m8DAQE477TS21rKA1lNPPUVERITv1alTp/rcynHj/MHebU1St+zjzaV/8FDWmbg9Bl2yl+Be+z64XbDlK8jZVq/rju48GovJwuaczfyW85uv3OPxUOYqY8KCCVzx2RU4XI2/Qqs1NoZ2d94JQNYzz7Dj2r+QfskllP3+u69O0dKlZE9/mZK1a3Hn55PzwQc4Dxxo9FhEROQ4ZQ3Bg3fe9uhY77QrU3gsveLCICgSgHCKACgodXJK3CkMaz+MAHNAlUsZhkHIqd5e7+Iahpt73G6Kf/oJd/le4CIi0rI0aHE1wzAqffZ4PFXKqpOSkkJkZCQXXnhhpfKhQ4dy9dVXM2jQIEaMGMGHH35Iz549mT59eo3XmjRpEnl5eb7Xzp07G3IrrV73mDDGD+mAy+3hic83MmdbMK9xMQCmz++GN5Lh/cvgvcvqtNp5hcjASE6PPx2AL9K/oMxVxpTlU5i2Zhoz1s1gTdYaNuzfwMYDG5vkvtpcdSVBJ54IgBEUBA4H2a+84jue8/5sAMLPOw9br17gclGwcGGTxCIiIschk8nX6337IG9RaFQHokICfD3eEYY38S4sc/Dw0If591n/5uS4k6u9XEjFPO8aFljL//JLtl91NVnPPdeYdyEiIs2kXol327ZtMZvNVXq3s7KyqvSCH87j8fDWW29xzTXXEBBQ9WlvpaBMJk4++eRae7xtNhvh4eGVXlK9Fy4bxBMX9ifIaibMZuHUCc9A9z+BsxQyyoe0ZW+GvfXbxuTsrmcD3sR7ZcZK/rvlv8zcMJO3NrwFwN9P/jsD2g5o1HupYJjNdHnnbXquXk3CB94kO/+LLyndvAX7rl0ULlkCQLvbbiXi/PO9xz+vfli8iIhIQxgVw81z/vAWhJYvaFqeeIdTDHgodbhxuCqvSeLxeCpNywo+1Zt4l/z8c7XzvIuWLwegdMuWKsdEROTYV6/EOyAggBNPPJGvv/66UvnXX3/N8OHDaz138eLF/Pbbb9xwww1H/B6Px0NaWhrt27evT3hSA8MwuHpoF5ZPOpMl9ydzUmJbHBf+m9z2I3D1vxS6eHuu2fR5va6b1DGJMGsYCREJDGw3kKv7XA2A2+NmTMIYru57dZ1GQjSUYTZjDg0hsFcvwsaOBSDrmafZ//rr4PEQctppBCQkED7Oe6x49WoceytvgVa6cSM7/vpXStavb7I4RUSklSpPvMnwtiHb7GF8tGYXBEYC3vnfIXi3Ri0sdQKQV5aHy+1ib/FeTnj3BMbOHYvH48HasSPm6Gjvft7V7NpRuv5nAJwZR57aJyIix556DzWfOHEib775Jm+99RYbN27k7rvvZseOHdxyi3exrUmTJnHttddWOW/mzJmceuqp9O/fv8qxyZMn89VXX/HHH3+QlpbGDTfcQFpamu+a0jgigwNoE+IdbXDem78wOP3/+LbPE6RFj/NW2PRp5RPydnnngNcg2BrMN5d+w4w/zSDCFsHEEycyosMIukV0Y9Ipk5rqNqrV7vbbwDAoWrac3P/+D4A2V/wZAGt8PEEnnAAeD/lffuk7x11Swq677qJo8RIyH3+i0ReEExGRVq4i8d7nnVb11C9teOLzjWANwm1YAG+v9/mD4vF4PJz38Xmc/sHpbC/Yzv7S/TjdTkqdpRQ7izEMg6CBAwEoPexhsLuoiLLfvOupOLKyqt3RI6s4i+ySbBzuxl9bRUREjl69E+/LL7+cqVOnMmXKFAYPHsySJUv44osvfKuUZ2RkVNnTOy8vj7lz59bY252bm8vNN99Mnz59OOuss9i9ezdLlizhlFNOacAtSV2c2MW71cmt763humXRuDBB5s8HF1n7+X/wYj9IfbrW6wRbg33vrWYrr/7pVeZdOI/ooGje/uVt7vjuDvYW7a3lCo3D1r07HV99hZDhw8BiwdarF6EjR/qOh5/tHRa/f8brFK/xrpif9eKLOLZ7/62W/vwzxSsbvsWaiIgch2wHVyj3GGaWuftzoMhOQZmTMqt3CtzZ3QN56YohRIXasJqtAGQUZrC/ZD8A+0v3M32td02boEHexLtkXeWVzUt//RUqkm2HA1c1i4XenXo3yR8m892O7xr3HkVEpFFYGnLSrbfeyq233lrtsZSUlCplERERFNeyCueLL77Iiy++2JBQpIH+1CeW937cgdPtIYdwVrp6M8z8q3e4+dBb4Ydp3orr50Dyg9CAIeOf/v4pm3M2c3bXs8lIz6B9SHvGJo5t5Ds5KCw5mbDkZNwlJRgWC4bl4D/vyPEXkjdvHqUbNrBjwgRsPXr4hvIFDhxI6fr17H/zTezp6RQtX07M3XcRkJDQZLGKiEgrcEjibXQ8mYA9kVBkZ8eBYroHRYL9AKO62nx1YoJj2JqzlazirEqX2Za3DYCgQd5V2krWVe7xLikfZl7BkZGJpW3bSmXr93nPefuXtxmTMOZo7kpERJpAg1Y1l5ZvRI+2XHdaAv83shuXndSRJeby0QVr/gM7V0JmeaOfux0O/OF90p69tV4rnw9q5/0F4ukfn+btX94mLiSusW+jWqagIAyrtXJZSAhd3nmb0ORkPHY7pb/8Ah4Pba6+mg7/ehZMJoqWLiXzscco+Oordv3tDtwlJc0Sr4jIsejVV18lMTGRwMBATjzxRJYuXVpj3QkTJmAYRpVXv379fHVSUlKqrVNaWtoct9M0Ag7Zk7v7KDpFeUeB7TxQjC00CoDh8RacLu/iajEWb/19JfvYX7rfd+q2/G0ABA4YAIaBY/dunNnZvuMlP1dOvJ17Nc9bRKSlUeJ9nLKYTfzjvH78fWxvHjy7D3fe/RAERXnnqX1wReXKv30Li/4JL58EK9+o83cMivEm3vtL9/te/mQKDqbjy9Pp9MYbdHz1VRI/+YS4hx8ioEsXwsac5a0TGoo5MpKyrVvZ++STvnP3Pv0Mf1w4HmdOjr/CFxFpNnPmzOGuu+7ioYceYu3atYwYMYJx48ZVmUpWYdq0aWRkZPheO3fuJCoqiksvvbRSvfDw8Er1MjIyCAwMbI5bahqH9HjT7Uy6RHsT7+37i30rm7/59Vq6P/Qln6/PoN3viwHIOrDVN9QcYE/hHkqdpZhDQwno1hWg0qKfFXO+zW2808QcmTVP4TJoukVNRUSk4ZR4C5HBAQRGxMA55XuDFpf/MtCzfFj4zx/C8vI9sr9/AZz2Ol13YNuBvvc3DriRUZ1HAZBvz8fpdjZK7PVlmM2EjjidsDOTCezV01fefsoU4qZMpusXn9PhxRfA+H/2zjrMiuqN45+5vd2d1BJLd3eLAgJiAQooiKKIgfgzUFTEACxEWkARaZAG6e6OBbY778bdvf37Y5a7rLQSxvk8z332zsw5Z84Z5c68877v95XIW7yEor17sRUXk/vTTxjPnaPgD4r+AoFA8G9k0qRJDBkyhKFDh1K9enWmTJlCWFgY33///XXbe3h4EBgY6PgcOnSI3Nxcnn322XLtJEkq1y4w8P5EQt0zrhjeTl4QXI8Ad/klQmaBkSJFaakxcwEABSUm/PNlT3VmXmy5l9F27CQUyC81/hhubsnKwpySApKEa7t28r601BtOyY4QChUIBIK/I8LwFjiw1+hNQWltbnyrQvt35O9JB8FSGnZdkAqnl93WeBHuETwb/SzPRD/DyHojAZh5ciadl3Rmfdz6uz39v4TSzQ2vxx5D7e+PS7NmePbtA0D+hg0YjhzBbpZVYotuEmopEAgE/wZMJhOHDx+mc+fO5fZ37tyZPXv23NYYs2bNomPHjg7h1SsUFhYSERFBaGgoPXr04OjRo3dt3g8EtwD5b6UOoFDiXVo5JKfIxLFMWQwtL0fO57bnp+Fvll9cZxRnUsG9AvX86zmGcuR5175ieMsCa1c835pKFdFWqgRc6/EWSuYCgUDw90cY3gIAzFYb7b7cTsszvdDXHQa9pkJATXANKGtUobX8d8+3t5XrLUkSoxuO5rWGr6GQ5P/VbHYbReYiZp+a/bcu3+XWsSMAhdt3YNi3z7G/aM9e7Kbb8/gLBALBP5GsrCysVisBAQHl9gcEBJCWduvc4tTUVNatW8fQoUPL7a9WrRpz585l1apVLFy4EJ1OR4sWLYiJibnhWEajkfz8/HKfvxW1H4eHvoTOHwHQuUYA0wc0YHjbSuhxAcBLIb+4VuYnEGa20MJQTEObihfqvsC8bvN4pNIjQFmet3N92RgvPnoUa2ERhVu3AuDSuDHqIDlCwJyWit1uJ04fh91ux2C+sYCtQCAQCP4eCMNbAIBaqSDM2xk9riz0GgahDWUl80pyeDiBtaHfj6B2hvSTcHT+nzpP/6r9cVY5E5Mbw87kv6/32LlxYyStFktqKnnLljv224qKMBw7dsN+lsxMbEVF92GGAoFAcG+R/lDNwm63X7PvesydOxdPT0969epVbn/Tpk15+umnqVOnDq1ateLXX38lKiqKb7755oZjTZgwAQ8PD8cnLCzsT63lnqF1hUZDwT0IgIp+rnSODiQqwA29Xc73DtDI4nHawiQqm81MS8/kNX2ZeGfDgIZ0iexCRQ85t1tTuTKayEjsRiMFmzdRsEUuD+bWsSOq0tB8S1o6U49P5eEVDzPr1CwUkoJwt3AATFbxclggEAj+jgjDW+CgS7R8Q19/6iqPRstXIaobPPI1OHtD4+fl/atGwsZ3y+qK3iYeWg8eq/oYAJMPT0Zv1N+Vud9tFE5OODeRld6t2XIennNpXfnrhZvbbTaypk0jpnUb4gYMwG55MDnsAoFA8Ffx9fVFqVRe493OyMi4xgv+R+x2O7Nnz2bAgAFoNJqbtlUoFDRq1OimHu+xY8ei1+sdn8TExNtfyAMm1yob3j5K2ch2NiQ7jtlzLmG3yveJ3lV680WbL+gYIUdaSZKEe/duAGR+9TXWnBwUHh44N2qE+orhnZ7OD8fkfPuvjnyFm8aNye0m0yqkFQ0DG96fBQoEAoHgjhCGt8BB5xoBSBIcS8xjZ0wmNpudPfnepPeYC8GleWgd3oOWo+Xve76GQ7Pu+DyDogfho/PhYt5Fnlr7FIn5f88HKdc2bRzfNREReJaq8xbukA1vu9VK/tq1pH38CXFPPEHmlK/Absd45iz61b8BUHL+gvCACwSCfxQajYYGDRqw6Q9ikps2baJ58+Y37bt9+3YuXrzIkCFDbnkeu93OsWPHCAoKumEbrVaLu7t7uc/fmRKzlZXHkpm3N44cm2x4eynkMHD3kjLDOwkztRfUo8uSLtjs177Adu8mG96WVFlEza1tWyS1GpWfH0gSdrMZ9z9El0d5RTG141TeavzWvViaQCAQCP4iwvAWOPB311E9UH6oGTDrAPU/2sSTM/az6OBVhrFCCR3fh07j5e3tn4HpzgxLXydffuj0A24aN+Lz41kSs+RuLeGucrXh7dysKS4tW4AkYTx/nvhnniW2Vy+SR79G7vz5lBw/gaTR4NJGzoPP+u470j/7nNiePUl+480HtQSBQCD4U4wePZqZM2cye/Zszp49y6uvvkpCQgLDhw8HZE/0wIEDr+k3a9YsmjRpQs2aNa859sEHH7BhwwYuX77MsWPHGDJkCMeOHXOM+W/AbLXxyi/HeG/laVJLZI+/p8JAu6p+RCjk6KlnA/3pHhYCgNFqRCEpsNgsZBoyHRU/tFWqoK1S2TGuW6dSb7hajcrXFwDvq9LdjVYjOSU5lFj+wTXRBQKB4F+OMLwF5ehdL8Tx/fXOVQFYcjgJm+0PQmhNhoNXJBRlwL7rl5e5GVW9q/J9x+8Jdwun2FKW67YpfhOzTs7it8u/PXCxGE1oKNoqVQBwad4clZcXPkOHgiRh2LcPY8xFFO7ueA8aSOC4cVRcvYrQyZNR+vhgTkoiZ/ZsAAp//x3TVeGRlqwssmfOFDXBBQLB35b+/fszZcoUPvzwQ+rWrcuOHTtYu3atQ6U8NTX1mpreer2epUuX3tDbnZeXx/PPP0/16tXp3LkzycnJ7Nixg8alaTz/Bly1KtRKOQ8+oVgLgAdFzHm2MWGSrG7uqlA72vs4+QDQelFr2i9uT1JBkuOYW6nXW9LpcGnRwrFfVRohMCxQrr7RtiCEM8OeofeM1gzZMASb3XZT8dL8jRvJX7/hL69VIBAIBHeG6kFPQPD3YkCzCIpMFrrVDCLc25lP150jIcfAsqPJ9KwbjMliQ6tSoFJpoN07sGwo7JoMqcfANwrajgWl+pbnAajjV4c1j65xbK+PW8+YHWMcYXehrqFMaDWBuv5178FKb4/gL76g+OhR3Dp1AsD/tdF4Pd4f/W9rkJQKPPv1Q+nhUa6Pz3NDyfh0IigUqIOCMCcnk/frYvxfG43NYCBh6HMYz52j5MwZQiZNehDLEggEglsyYsQIRowYcd1jc+fOvWafh4cHBsONX5hOnjyZyZMn363p/S2RJAlvFw3p+UbqVYmABFCZ8sFqAb1sVPu5hYBJzp/30cmGt7fOmwJTAVnFWUR6RALg2acvBevW49apEwonJ8c51AEBlAD1pQjW9l6L5e0JlOzaykMmifnuJ6gzrw77ntyHi9rlmvnZSkpIfu11sNtxbdUShcu1bQQCgUBwbxAeb0E5dGolozpGUTXQDSeNkofryG/WX198nOrvrif6/Q3EZBTKjWv2kdXOTYVwdjXs/BIOzfnT567nV48nqz1J18iuBLoEklSYxKD1g/jm6DcPrEaprmoUXo/3L6fkqw4JwXfY8/gMHXqN0Q3g/dRT+L/5JuGzZ+H/1hgA8pYtw2Y0kvLWWIznzgGQv249xosX789CBAKBQHBf8HaRPd2dG8pRY5K5CPLiwW4FpQY/ryqOtlc83lcM8OySbMcxdYA/FVevwu/lkeXGv6JsrszKJcw9DHu8HFEVlVzm5S4yF2G3268JPbfm5IDZDBYLlty8u7FcgUAgENwmwvAW3JSx3avzeKMwfFw0WErDzdPyS2/kCgWG/r9Cn1llaufbJkDJn1MqD3AJYEzjMXze5nOWPbKMhys+jM1uY/qJ6QxYO4CckhxHW6vNysJzC5l8eDJWm/UvrfFuI6nV+Ax+FpemTXFr2xaVnx/W7GwutmlLwcaNoFajq1ED7Hayvp/2oKcrEAgEgruIj4uc251p0jr2vTKltASnRxgB3mW521cM7isGeFZx1i3H14TKKWGnjm5i1rEZmOLlkP+KaaC2yPdpg9nAO7vfoc2iNqQXpTv6WvVl92drXt6dLk0gEAgEfwFheAtuirtOzad9anPgfx3Z+WY7Tn3QhXZV/SkxW3l10TGaTDlBdoWHocsE8K0KxTmw86+HT7tp3Pik1Sd83uZz3DRueOm8yDfKSjJpRWk8t+k5Ptn/CbNPzWZPyp6/fL57haRW49mvLyA/5ChcXQn+dAJBH8nidPlr15K7cCGmxETs1r/XCwSBQCAQ3DnepYb3nlg9ttJw7yr2ywDYPCPw84t2tPVRycrnvk6yYFp2cTa3QhsVJX+5FM/P277CbpLrdqutUFEWQcdgMbDq0ioMFgO7knc5+l5tbFv1Zd8FAoFAcO8ROd6C20KpkAjzdnZsa1UKLmYUUmC08MvBRF5sVxk6fQgL+8tiaw2eAe8Kf/m8XSO70iqklSNXTW/U88z6Z0guLCvLklOSg8VmYdimYVjtVmZ2nolK8ff5X9vnueeQ1Go0kZG4tmuHQqcDwLVjBwo3byHtgw/lhkol2qgoQj7/DG3lyjcZUcZ48SIKN3fUAf73cvoCgUAguAOuGN6LDycxWqcjiCKipTgATK6h+JfmcANEWGUP9RXD+3Y83tqqcgh7QC5UTCsvolYtyc75MIkicxE6pY4SawlNgpo4jpczvIXHWyAQCO4rwuMt+FNIksQzzSMBWLAvHovVBlFdoEIbsBph3Ztgs8LeqXBgBtiurVN6u1wxum12G2N3jiW5MJkQ1xB+eegXjg44Ss/KPdmWuI0DaQc4nH6YE5kn7sIK7x4KJyd8X3gB927dHEY3QPCnE/F96SWc6tUDtRqsVoxnz5L4/DAsWTd/+DIlJRP7aB/iBw7A/heurUAgEAjuLn0bhPJye/nlaZHkCkAtRRwAxS4hBDgH0ELhxqMFhbQrkcuHXS/H+0aovL2xerujAJqelw1ve6kMSdUkeTunJIcSq5wW5qXzcvQt7/H+c2lhAoFAIPhzCMNb8KfpUScIHxcNqfoSlh1JBkmC7l9gV6ghZiOG71rBhrGw9nX4dQAYC//S+XYl72Jn8k4AJredTLRvtMOz/fO5nwHoFNGJ+gH1/9rC7hNKVxf8XnqRyIU/U+3YUSpt3IAmIgJzSgqJL4zAFB9/w75Fu3ZhN5kwxydQfPz4fZy1QCAQCG5GzRAPWlSWPdhFCtnw9pVkI7fQKQRPnSfTIvvyQVYOZw9v443Fx6nkWYnuFbrTNKjpbZ3DEOEHQP2LsqGdGS0LoUYl28Fud0SFaRQaNAqNo9/99nib09LKldMUCASC/zLC8Bb8abQqJcPbVALgsw3nyC8xg18UJyMGAeCcfRq7UgtKDZz7DX7sAaY/X5t7ecxyAMa3GE91n+qO/edzznMw7SBKScmbjd78Cyt6cEhKJZrwcMJ+mIbSw4OSkye51P0hkl5+hcypUzEcPlyufdH+fY7vBZs33+/pCgQCgeAmGEyyZscmp+7gFUmu5ME5WxhpPqVh3yENAHDPOcHiw0n4a6oysfVEBtQYcFvj54V6AqArLfjh1q07klaLezG8djwUv62nkOx2TDYT4/aOc/Sz5pV5uW332ONtt9uJe6w/sb16YysuvqfnEggEgn8CwvAW/CUGNY+kop8LWYUmpmyKASCnwUjOKyqSYvdmftS38MwacPaBlKOw8kW4sBGmt4NtE8sGyjgHFuNNz/VJq09Y03sNvSr3cuw7nX2avqtl8bL24e0JdJHLrNjtZXlveqP+gZUju1M0kZGEz5+HS5vWYLVSsHEjWV9/Q/xTT5Oz4CdAXpvhwEFHn4LNm8ut90bcThuBQCAQ/DX0BjMLD8hK4wfdOsArx3kx+Fe6miZyrqBU6Ty4LnYkQqUsfNHLL67/gM1uY8GZBddNn8oIdiq3XRjqhVPt2gA0WRdP5e/W0fSs/JufW5LraHc/Pd62oiIsGRny3+xbh9ALBALBvx1heAv+EhqVgnEPywqt3i5qbDY7bWtGkvjob7Qwfs0XZz0wBNSHx+aDQgWnl8HP/SDlCGz7BM6tgc3jYGoTOST9JjipnAh3Dy+3L9ytbPvxqo9zPuc8o7aO4sUtL3Ix9yITD0ykzaI2PLXmKfJN+Xd9/fcCXVQU4T/8QOTixfiNGoVr+/YApH/0Edmz52C6eBFrdjaSVouk0WCOT8B4IQbD0aM3fLjJW7ac87XroP9tzf1cikAgEPznyCwsYeMZuYSXi1ZOh2pfzZ+2Vf0I8SzV+dB5kOMUCUBtxSXyDGbMNjMZhgzHi+J9qfuYeHAiz6x/hm2J28qdIzFAWW47yduG16iRuHXrJperpCz/+8aG9731eJfzrhcU3NNzCQQCwT8BYXgL/jKto/wY0DSC7RcysZV6VdvVCCbM25X8EgsrjqZAZAvo+qncQVJAcGke9pLBsGuy/P34L1B0a0XXq3HTuPFVu694r9l7NApsBMCWhC3sTN5J71W9WXB2AVa7lbM5Zxm9bfQ/yuvrVKsmvsOHEfrdt/gMHwZAxmefkfn1N/Lx+vVwadECgISBA4l/4kkutu9A2viPsGRmOsax22xkTZuG3Wwmbfx44XkQCASCe4iXc1lOtVopq54NbVWRuc82pn21AMcx7yg5n7uO4jJ5BjPtf21Ph8UdSMiXveUxuXIUmdlm5tWtr7IjaYej7/MPfwAq2ajPc4EJZ76mxanBJL/Zn8APxgHQNF6L2mIn1/jXPd42k4nCnbvuKGTcln9VzXBheAsEAoEwvAV3h/G9arJ4eHNUSvl/KaVCYmCzCABm7rpMQYkZGg2FActh+G4YvB4Ca4FFVl1F5wFWExydf8fnbh/enn5R/ZAkiareVRnbeCz1/OuhUqio4FGBd5u+i7+zP4OjByNJ8kPQqkur6LKkiyNv/O+MJEn4jxqF5+P9ASjYtAkAlyZNcOvYEShVp1UqsRuN5P70E5d79aZo334ADAcOYk6QH+Rsej0Zn33+AFYhEAgE/w08rzK864d73bCdVJrn3Vgdi91ux1vnDZSVFIvLjwPATe2GxW5h1slZjr4+bgFoK8kaK95RtfDQegCw6Pwi2h4fTLGXM1JxCTXj7Df2eN9BjnfuTz+T+NxzZM+efdt9rh7fVvjXxFUFAoHg34AwvAX3jH4Nw/B0VnM5s4jVx1Nl1fNK7SGgBqi00HcOhDeHTuOhyydyp0Nz5DJkf4Enqz/JvG7zOPjUQVb1WsVjVR9j7aNraR7SHLvdzowTM/jfrv+RUpTChAMTbqtu6t+BgDFj0FQoq43u3LgJ7t264t69O15PPknlrb8TPncO2qpVsWZnkzB4MDnz5pG3eDEATg0agCShX7kS/erfyo1dfPo0ectXiNJkAoFA8BdRKiRHLe82Vf3KHUvOK+Z4Yp68UWp4N9PF0a1m4DW1vOPz5coW/av1p3Vo62sUz7VVowBwr1zNsc9F7YLBWkJCXVnvpOUZO/3WFRD/0ovYzeZyxvCdeLyNMbL33XTp8m33serL0rtEqLlAIBCA6kFPQPDvxcNJzYIhTdh4Jp0nGocBYLPZWX40mQX743m4djCDB6+TG5sMsOFtyIuHje9AeDOI6goqzU3OcHOulBoD0CplQRtJkmgX1o65p+eikBTkGfP49ui3jGs+7k+f536hcHIi+LPPiHvySZQeHjjVqomkVhMy6UtHG7W/P5G/LCRt3AfoV64k/ZMJ8gsPIGDsWPQrVpC7YAEpb7yBKTYW35EvYS8uJvG557Hm5GC8GEPAG288qCUKBALBvwJvFw05RSZyCk2OfetPpTF8wWGig915tWMUq46YmaTUoSrOhaRD+DiV1vIultOBvmr3FfGZJwn2isLb2dcxjt1uZ/LhyYTV1VHvbGU8ej6C4az8MvWK8f5rQBzvAq1Oy+lVBn7HcPQo1vwyY9ian4/dZkNS3NoHY0lLlf9m3f6LauvVoebC4y0QCATC4y24t9QM8WB0pyhHiPeJZD2vLT7O0YQ8pm67WJZzrXGGeqVlVPZNlet+L3hUrv19ejmsHgW5N65rfbvY7XYWnF3AqAaj+Lr91wAsv7ic01mn//LY9wOnWjWpuHIFkb/8gqRWX7eNwsmJoE8n4Dd6tLzDbkdbozpONaMJGPsW3s8+C0DW1KnkzJ5N7sKFWHNyAMiZNZucn366L2sRCASCfytWm3xvS8ory4luGCmHnZ9OyWfmrsusOpXFaY+28sGj8/DRyYZ3Vols3LoVZFBz1iN4rxtbbuxiSzFzTs/hQ/NyCmZ9yFT7Nocg2xXD+0y4RKGu/JxKTp2Gq6OabLbb9kSb02SxuKv1Q65H7i+/kDTqVewmU/lQ8wJheAsEAoEwvAX3lRk7L+OmVaFUSGQVmki+6qGENm9Cu/9B7f6gcYW4nTClFix+Bg7PgZkdIPHgDce+HSRJYlzzcfSL6kc9/3p0iuiEzW7jnd3vlBNe25O8h88Pfk56UfpfOt+9QFuxIprQkJu2kSQJ3+efI3jip2giI/F7aaS8X6kkYMybBLwtP8hlTPmKrOkzAHBu3BiA9I8/wXDwIHazmfTPPyf+6QEkDH2OnPkL7uGqBAKB4N9DbFYRAIfichz7fF21tImSQ8/3XZb3b3fpCoDx2BJ8Ne4AZOeVvmROOgQ2MyTsBWR18uzibIdYmlapJa0ojR/P/Fh2jlLD26qUmNNRgbljM1w7dgCg+PhxABTOzkjOznK7G4Sbm9PTSRg82FGu0pyWBtza45017QcK1q/HcPQYtqu867ZCEWouEAgEwvAW3Ffe6lqN3WPbUz3IDYATSXpOJetpNmELB9OssvH96HQYuEoWXCvOAaUWvCtCUSb82APS7553+p2m79AypCVvNHwDSZIw28x8feRrhm0exrwz8+i7um85Jdl/Gh49e1Jp/Trc2rcrt99rwADcOnUCsxmbXo86PJzwWTPx6PkI2GwkvzmGlLFvkzNrNoZDhyjatYv0jz/GcOjQA1qJQCAQ/HPYMKo1w9pU5H/da5TbP/Wp+gwqFR6VJFBWaEmsLQCtzYDv8V8ByI79nQOpB/jk8hK2ODtBfgqTDn1J60WtmXt6LnkleQB4aj1x07g5xtYoNLiXGu8AO2spcB7/Ns4NGwJQfEKuB6709ETpKYux3UhgrWDDBor27CV77lxshYXYDQZAztW2lZRct4/dbsdaWjXDkpVZrpyYVXi8BQKBQBjegvtLmLcz7jo1dUI9ATiemMfPBxIoMVspMV8lqhbaAAZvhFavw4i9MGwnRLaSVdD3fHPX5uOt8+b7jt/TPKQ5ACpJhZdODgcMcgkiz5jHi1teZNyecf+YOuC3gyRJBI3/EFWAXNrG94UXkNRqAt59D3VEOJbUVPJ/+w2USvzHjJGNdCDtgw+wm80PcuoCgUDwt6dqoBtju1XHw7l8SpCLVsUHPWuy6qUWzBvcmFqhniy2tgWgUvIpHiosonl+LgeTd7Ew/xw7nZ3AbiVE5QpA7NllZMbKL4O9dF7lDO9OkZ1wVjuXO5+Xzgt1eDgAllQ5T1vp6YnSwxOQPd6Go0cxJSaW62eKTyj9G4+5tN8VbuT1thUZHPcHa3Z2uXxyIa4mEAgEwvAWPCDqhHkCcDQxj81n0sk1mB05cQ78q0GHd8GnEmhdoeMH8v5TS6Hw5nlmfxaTzcTWxK1MaTuF33r/xtPVnwZgacxSHl356N8y9PzPovT0JOKnBYRMmYJHr57yPlcXQr74Ekrzx4M+GIfPs88Q+OEHKL28MMZcJPn1N8j87juMl29f3VYgEAgEZdQO9aRVFT+8nDUssbbGiJqaZiuf5hQwML+AuOyzAESaZEO2gsIJgFhjNnu2ymU3vbRlhre7xp1PW32Kl9aLcLdwx3nmnZnHE8dHlzv31R7v4hMniX/qaWIf7VPO+DbFy+Hu1swsTJculetvybj+/deaWxZWb8nMKq+gXiQ83gKBQCAMb8EDoXUVP75+oh4Dm0WQUWDERaOkWSUfzqcV8NFvZ8guNF7bKbSBXH7FaoLDc8v2m4rAkHNt+z+BVqlldpfZdIjogEapYUzjMcztOpdwt3C6RHYp5134N6AJDcW9axeH+B3IAm6RCxcSPu9HPPv2BUDl5YX/668Dcghi1jffEvdYf0eJmauxW62YEhLK5cwLBAKB4Fo8ndVk4MUT1g9h2HYIiAYgrrSUWKTZAkAFa6lYm0qFRSXnj3vqPHFTy/ekQnMhNruNcPdwvmr3FQAeWg90Sh0p7hbsV/3Gy4a3JyD/nl8RWUt+ZRQ2o3zvNSUkONoX7dtfbs6WrDLD2263Y7fIc7Tm5l7VJqucqrkQVxMIBAJheAseEIEeOh6pE8y5VDn8rE1VP7QqJW8sOc7MXbGsOJZy/Y5Nhst/D82SS5BlXoBvGsCk6nByyT2Za4OABqzstZI3Gr1RLozPbDPze8LvHMs4BsgPIOtj1/9jFNJvhlPNaFxKxdau4PFobwLHvY/3oEFoa1THVlhI4rDhmDMyALCZTOQtW87lh3pwqXMXMidNBsCSm0v++vUOcR6BQCAQyFwJRT9ijqDEpwZ4RZKoUnLOIId3R1hkj7dnYS5ONrBJEqdc5H2VPCo5Xgbb7DYMZjkP22q3UtWrKpU9K+Op88SikijwLpM4l0PNZY/31S9PS86cIePLL7GbzZiTkx37i/btLTfnq5XNMydN4nz9BhhjYrDkXOXxzs7CJup4CwQCQTlEHW/BA2XTGTl0u1MNOde4X8MwTiTp+fVgIoNbRLLiWDLNK/kS4F760FCjF2z4HxSkwvfNZW93kWz4sXQIZJyBdu/AbdQlvROurgm+8NxC9qTs4XzOeVKLUlEpVKzsuZIzOWd4Y8cbeGg92PbYtnJ9/g1IkoTX448DsjEd//gTmOLjudS5Cy7NmlF87Fg5j0f2zJloK1cic+pUzKX5gk716hH6zdeofH2vew6BQCD4L3GlyofVZkdfbEbnGcHgoADH8dBSj3dJVgLhZivntUqe0eezv8JkHq/WGa1S62j71NqnWNlrJVW9q7LkEflF9Pq49QDk+Khxz5ariCg9PbHbrtJUATwf70/eL4vQr1iJ1xNPgLXs+JXf7ytcMbwtOTnkzP0Ru9lM0b79KFxcytpk/SHUXNTxFggEAuHxFjw4tl/I5Hy6/Ba8XVV/AB6pE4xWpeB8egH9p+/j1UXH6f/DXvTFZjLySziWaoC+s8EtCHJjZaM7oBY0e0kedOeXsOhpMF7n7XpxXvkapn+SQ2mH2Ja4jdQi2SNhsVn49ui3NAtqBoDeqOdoxtG/fJ6/MyovL8J+mIY2Kgp7SQmFW7dizc1FFRCA/+uv4fHoo2C3kzLmLczxCShcXUGSKD56lOxZs687pjkjA3P6vyeHXiAQCG6FJEkEuusI8tBhMFnBK4KPM2Vl8LolRq5IszkXxBFlktXEk9USuqJwPLQeSJJEv6h+AIS5hQHQdWlXmv3cjLSiNLy0slhoqvfVoeYeDnG1K/i9/DKSToctP5/C7duvO1eHSFupuFrekqUOMTVLRnr5UPP0DGxXGdvC4y0QCATC4y14gJgsshFcM8QdT2cNAB5OarrWDGTlsRQOxMpha12iAzmXms/guQfxdtUw55nGVH5xP2z/DPJToPsX4OIDgbVh1Ug4vwYW9IHBG+R6LQAnFsOKF6BmH3j0h78070HRg2gQ0AA3jRvh7uE8vfZp1sWt49maz/JIpUdYdWkVvyf8TqPARuX6lVhKuJB7gRo+Nf4V3nBNZCQVVq6g5ORJCnftQletGq6tWyOpVNiKijAcPoQ5PgF1aCgRP86l5PwFkkaMIG/pUvxeHonh0GFMsbF4Pf0UtoICYnv2wm4yUWHZUjQREQ96eQKBQHBf2P1W+7KNvAgalxhZnZiC51UviqXEfbTS2HG32ahlNLIsIwloAuAQU7tSSqzAVEChuRCDxeCo0pHkbnGMpfT0xG4tG1tTuRIqb2+catbEcOgQ+pWr5HPqdNivKh3mVDMac0IClsxM7FYreb/84jhmTk+Hq3Q9rpQVu4KtqAi7zYZ0l6PRBAKB4J/EP//pX/CPpVONAH4a2oTqQe7l9r/dvTpBHk6EeDlRP9yT6GAPiowWXHUqEnOK6ThpOxV9Xajo15taIR4MwA1vgDr9ZQX0uT0gcT+kHIWQ+nBmJSwfBnYrnF4OPSaBxuW6c7odavvVprZfbcd2twrdWBe7jj0pe+gQ3oFVl1axJWELbzZ6E0mSyC7O5tMDn7I9aTvFlmLahbVjSrspKKR//gOIJEk41a6NU+3a5fYrXFwInzED/YqVeD7WD3VgIKrAQNShoZiTkkj7cDz61avBYkHh4ow5Lc3hLUkZ8xYRPy1AUipvef6iAwdIe/c9/N98A7cOHe7JGgUCgeC+4RUJQGSpYBluQVCQisJcRDczdCuS87jnZ5fpoBgs8r4rGiRXSl/OPTWXkfVGAhDrUSZYqvT0xG4pCyV3rlcfAKd6dTEcOoTxrKyo7tK0KYXbtjna6WrWIn/tOqyZWRRu3445pWwOlvQMJHX50mkAklote8XtdmwGA0pX1zu/JgKBQPAv4Z//5C/4R9Oisi/eLppy+wLcdbzVrRoDmkYQHSwLwLhoVcwa1Ih2Vf1QKSQuZxWx+Ww6kzdfoNXE34kpDVkntCFU7Sp/P70MMs7CkiGy0Q1gNUJpDdS7xaj6o/ilxy8MqTWE5sHNcVI5kVqUypmcM9jsNsbsHMP6uPUUW+T8uq2JW/n26Ld3dQ5/RzTh4fi9PBJ1YCAAklIp5w4C+uXLofTBMmPSZHLmyeVxUCgoPnaMjM8+L/dQdz3sdjvpn0zAFB9P6rhxWAuL7t1iBAKB4H7gEQqUhYVnetW9brNmARZH5YjlF5cDcDHvYrk2OSU5eOo8qedfj/BqZWKZV6uag6y9AeBUt/y5XFq2LKeX4lSrJiDneOctXQaArvSlqyU9HWtOLn9E6efrMMhFuLlAIPivIwxvwT+GmiEezHm2MYfe6ciPgxvzwSPRRAe7E+HjQiW/q96iRz8q/z29ArZ+DDYzVO4EjYbK+8+vu6vzCnYNJtpHLgGjU+loGdISgE1xm7DarFT0qIiTyom5XefyccuPAZhxcgbzz8y/q/P4J+DZ51EkrSwGpKtVC02FClizs7Hp9WgiIwkaPx6AnB9/5GL7DiQOG47NZMJut2M4eBDLVTmEhVu3Yjx3DpBrzWbPmnn/FyQQCAR/kVm7Yun7/R5+PZgIKi24BzuOzbnkRon9Wk/y0zWdkUxFcOJX0orkihGX8y6Xa+OmcUOtUDOv2zze7/0VSLKhrtTYHHW8QfZ0w7WGt7ZyZdShoQAo3N1Rh8spQJbsbIp27wbAZ8gQQNbosOZcW9ZT6e6Bwk1WXrcKw1sgEPzH+VOG99SpU6lQoQI6nY4GDRqwc+fOG7bdtm0bkiRd8zlX+sB8haVLl1KjRg20Wi01atRg+fLlf2Zqgv8Ans4a2kT5Mah5JKtfasmPgxujUMgeArvdztKC6lhUzqBPhLOrAQk6j4eq3eUBLmwol4t2t+kQLoc8P1LpEdRKNW83eZvVvVbTIKABj1R6hGdrPgvI5ciusCdlDz+d/YnN8Zv/1fWvlZ6e+L/5Bi5tWhP67bcEvD3Wccxn2DA8Hu1NwP/+J3tRlEoKt28n9a23SHnjTeIHDCS296OYkpKx2+1kTf0eAKc6dQDImT0HU2Lidc9rMxgwJSVf95hAIBA8SJJzizkUn8vlrNKoHc8yjYsUuy8pdp+yxrpSg7kwA3ZPgWXPMdpD/g38qOVHALxS/xX8nf0dYeYAioxj+NUswLNSEWpbAurgYJQ+PmirVOawJoULuRdQ+figDgtz9NFEhKOJlOeiDghA5eMt66bYbNhLSlCHhODaSn7RbDcYHL+/V16uAig9PGRxTSgntiYQCAT/Re44x3vRokWMGjWKqVOn0qJFC3744Qe6devGmTNnCC9VvLwe58+fx929LJfXz8/P8X3v3r3079+f8ePH07t3b5YvX85jjz3Grl27aNKkyZ1OUfAfQqGQ8HMru8l/veUikzdfQKmuSy/lHnlndG/wrw7eFUHtAoVpkHocguvekzl1juhMrD6WCh4VHPsCXMrKw7xa/1Xq+NWhXVg7x741l9ew6pIsaDOx1US6V+x+T+b2d8D7qafwfuopANQB/viOeAFzRgYePR5CkiS8BzyN94CnKdqzh4Tnh5G/tixCwZKWRsIzz6CtWpWSU6eQdDpCp35H8qhXMRw8SFzffvi8MBzj2XOYkpIIfO891CHBxD3WH9Ply/g8N1RW771OLqJAIBA8CDxLa3nri03yDq8ISJDvXzrfCFJzfaiI7NUmrAnEbMRakIZNn4IaeDYrnf6D9jtyvIfWGsqQmkOQpLKQdduFDbjVLERrhy0XV2F11tFhwwaO5Z5k2ObnUEpKjg08hlO9upgTE5G0WlQBAWgiIynasRNVYCCSSoXS29shnObaoT0KZ2cU7u7Y8vMdHm9tpUqUnDkDgNLdHVtBAWZEqLlAIBDcscd70qRJDBkyhKFDh1K9enWmTJlCWFgY33///U37+fv7ExgY6PgorxJOmjJlCp06dWLs2LFUq1aNsWPH0qFDB6ZMmXLHCxL8NzkUl0PkW2uYvPkCAGusTQGwI0GbMXIjlRYqlRq7696EdWNg6wTYPx30d88bqlaqeaneS+Ueeq5GkiQ6hHcoJ65Wy7eWQ7Dt53M/37W5/BPwe/llgj/66Bpj2KV5c0foudLLi5DJk1CHh2NOSqJwyxYAfIcPQ+XjQ9CECehq1MCq15Px6UT0K1dSfPgwiUOHkvzaa5guyyGY2TNmkvDsYGxGI3eK3WymYPNmES4pEAjuKl6lhnduUWkU1FUe78CwSuU83nHO8n1i2+HTFCadknemHMWZ8vebq+8/3x79loYpK/jB04NCSWJU7n5e2/4a8dZ0VifKdb6tditF5iJHuLk6LBRJocC5YUMAnGrVAkB1ldPErUNHuW1A2YtlAG1UlOO7wsP9qlBz4fEWCAT/be7I8DaZTBw+fJjOnTuX29+5c2f27Nlz07716tUjKCiIDh06sHXr1nLH9u7de82YXbp0ueWYAsEVLLay8OzHG4Vx0aMFC2ydudTgHUq8qjBp0wW2ns+A6g/LjRL3w/5psP1TWPcGfFUHVowAfdIDmf/j1R7nq3ZfoVKoOJ55nLPZZx/IPP5uePbuRcW1a6i0bi3u3boRMXcO7j164DNsGJFLl+A7fDgAmtAQIhf9gu/Il9DVrInXU0+hrVIFS2YmRdt3gEqF36hXULi6Yjh0iNyFC+9oHna7ndR33iHppZFkfPHlvViqQCD4j+JRWk4z72qPN2CzS1SuXIUUZMM73+5MppMcSRUppeFlzpDb28xyFY8b4GwxYZYgRaUi/qoXnMsuLGNPStlzVmJBIh7du+PSvDk+g+XcbffOnam8ZTO+L70IgNJXnovCwwPnBrIauupqw1uhQFOxomNT6eGBwu3moeY2o5HCHTvKaXgIBALBv5E7CjXPysrCarUS8Ie3mwEBAaSlpV23T1BQENOnT6dBgwYYjUbmz59Phw4d2LZtG61btwYgLS3tjsYEMBqNGK/yWuXn59/JUgT/MppU8Ob51hUpMVt5r0cNzqTm46xpQmV/N77ZEsPXW2II93am2SuPouujkg3sEr38yTgDCXvh2E+yIFuHd6Hx86C4dTmru4mvky+dwjuxLm4di84vYlzzcQBkF2cz+9RsXqz7oiOU8L+E9qqHOHVwMCFffH7ddpJajd+LL+L3ovyAaE5PJ/6JJzGnpBDw5ht4DxyIyteX1HfeJXvaD3j27UvJyZMU7dmD8UIMTnXr4PvCC9cdW79suaO2bcGmTQS+9+415c7sFguWrCyHirtAIBDcDp5OsjGcZyj1eHvLv3lpeBHp78UxjT/YIM4egJO3LLxWSZFafpCEvRDR/LrjB+enA5Dq7Emcqewl9Y9nfnR8/7n7z1TyqIRSqSZ89qxy/dUhIY7vp0ihIpBTvwKSSn6EVAX4O44rPT1R+Zd5xZXuHihdZY+3rfDaaKG8ZcvJ/OorLOnpuLZpQ9gP0667BoFAIPg38KfqeP8xhNZut98wrLZq1apUrVrVsd2sWTMSExP54osvHIb3nY4JMGHCBD744IM/M33BvxBJkni7e3XHdu1QT8f3Z1tWYMH+eBJyDEzfFc/LHfpeO0DiQdj4DiTug/Vvycrnj84At4Br295D+lfrz7q4dSyNWcqYxmNwUjnxe+LvzDszj93JuxlcazCJBYlU8axC58jOtx7wP4w6IIAKK5Zjiotz1Bn36NWL7FmzMcXGEtenL6b4eEf7wu3b0dWqjUvTJuQtW4bxQgzWnGwsObkUHy3zJllzcig+dgwkiewZM/F/bTSaSpVIemkkhdu24fvySHxfeOGmv18CgUBwBa8rHu9Sw9sQUJ/5loc4ZqvMp17OnHNvwcGcrSy0tOdV35DrD5Kw/9p9VjPE7yH48m7QQLJaTbx3CKAHZAHQVZdW0a1CN2r51brlPA+mHWR65QQeS5W43M6TZqX7rw41V3p7ofK9yvC+Slztj6HmhsOHSX37bcd24Z49WAuLULq63HIuAoFA8E/kjkLNfX19USqV13iiMzIyrvFY34ymTZsSExPj2A4MDLzjMceOHYter3d8Em+gZiwQuGpV/O+hGgDM2HmZQqPlmja2kIZc7PEr9ocmg9oZYrfDtJaQc/matveS+v71qeUrPwCdzzkPQKR7JH5OflzSX+J/u/7HtOPTeG37a+xJFqkYt0Lp7u4wugEklQq/UaMAZKNbqcSjd29cO8hK9Onjx5My5i3S3nuf3AULyF+7DsO+fdiNRlxat8L9oYcAyN+wgZSxYyncupXkV0dTsH49hdu2AZD19Tckvzqa+AEDSRg8WOSECwSCm+LprEanVqBTy49kdhQoOn9EcPPHcXdSMfKRlvQzjWOZrTVeAeUNb2NgA/lL4j6w2coPvOhpmPcIwamnAci0FnNRJxvBr+oqMKr+KMY2Hkv/qv1vOUeT1cT4feO5HCTx6WNKDqjL0rJU/mXPaipPL1S+ZTnpSg/3slDzP/wWXilJ5tKmtaymbjZjOHCdFwj3keJjx0gY+hzGq55RBQKB4G5xR4a3RqOhQYMGbNq0qdz+TZs20bz59UOcrsfRo0cJCgpybDdr1uyaMTdu3HjTMbVaLe7u7uU+AsGN6FEriIp+LhSUWPjlQAIAJWYrG0/LL3w+WnOWjpN3McfYDp7fDn7VoCgDtn1afqC8BEg6dM/KkUmSxPcdv2fJw0uo618XgEaBjVj88GJ6VOxBbb/a1PGTS8eM3TWWrOIsAGL1sUw7Po3T2afvybz+Tbh17oTHo4/i0rwZFRb/SvCETwj+dAJKP19M8fHkr1kDKhXezzxDwNi3CP78M8LnziHs++9x6ySLCeX+9DPmePn/I2NMDMlvvAmUlTYrWL8ew8GDFO3ZS86cOdiMRhKef574gYOw5uU9kHULBIK/J6FeTpwb341tb8jiny5aFc+1rsi7PWogSRIB7joAnDVKXF1cQVtWgzslrIdcraNED5lXlWnNjYML6wEJn+h+aCUVNuy8UKUfS5NS6ZGTjp+zH09Wf5Jgl2BmH/ueH+e1g73fXXeORquRWr618Cg9d3x+PAazAfhDqLm3NypfX8e2wt3dEWpu/UOoueHwEQDc2nfAtVUrAAp37LjTy3dXyV30K0W7dqFfteqBzkMgEPw7ueNQ89GjRzNgwAAaNmxIs2bNmD59OgkJCQwvFTkaO3YsycnJzJs3D5AVyyMjI4mOjsZkMrFgwQKWLl3K0qVLHWO+8sortG7dmokTJ9KzZ09WrlzJ5s2b2bVr111apuC/jkIh8VyrioxddpLZu2Lp1yCM3lN3czmriHceqs7s3bEAtKvmD74u0HsaTG8LJ5dA27HgXQGMhTCjg2yQR3WDxkMhNx58q0CF1nJY347PIbA2VO/xp+fqofVwPNxcwcfJhwmtJgBQYinhybVPUmgqJKs4C18nX2JyY/ju2HesurSKNb3XiDDnmyBJEsGffFxun9LNjYA33yTljTdBpSJ0ymTcOna8pq9Ly1ZIajV2sxwS6tqmDYXbt4PFgsrPj/DZsyj4fStFu3ejcHcjd958cub+iDkjg6IdOwFIevkVwmfOQNJo7v1iBQLB355b/V5nFMh6No7Sma5+YJTDxeNUkVQIbShHaZ1bAwFydBcnfpX/VmyD1Gc6QcsfJi4/jhyPEJqYzZB+HkxFoHEh3ZDO5ONTCbRYGLTzS2g6Qq7XfRVuGjc+bvkxeSV59FnVB51KR2ZxJhHqiGtCzZVeXqBQgM2G0sPzKo93Wai53Wym+PhxAJwb1EcV4E/uzz9TtHPXLVMN7yXmVDl33pye/kDOLxAI/t3cseHdv39/srOz+fDDD0lNTaVmzZqsXbuWiAhZhTM1NZWEhARHe5PJxOuvv05ycjJOTk5ER0ezZs0auncvq1PcvHlzfvnlF9555x3effddKlWqxKJFi0QNb8FdpXe9EL7ceIFcg5mt5zNoFOnN5awiPlojK4g/Wj+ECr6luWXB9aBSB7i0BXZ/BQ9PgcNzZKMb4MI6+QMgKWHwerj0O2yfCBo3qHJJLl92D9CpdHzV7ivcNe4OA71psFw+LbEgkWOZx6jnX48CUwFPrHmC2r61ifSIpJJHJdqEtUGl+FPSDv963Hv0QFJrUIcEO0rn/BGlqwvOTZtStHOnLPT2zdekvfsu+pWr8H9rDAoXFzwe7oHHwz2w2+0YDh3CeOYs+iXyi0ZJq8Vw4ACxffuhcHHBuVEjfF96EXNiIukTJ6IODMK9e3csmRkYjhzBVlSEJCnw6N0bl6a3/j205OZiKypCExp6V6+NQCC4P2QXGll+NJl21fyp6OuCJElkFJQAcpQWAK4BkH0RAJeQaPB/TDa8d3wO1bqDfw04Xlq5oc4TALQIaUGUVxQuHqHg5AXFubJXPCCaMLcwANKVSozF2WgLM26ob+Kp82Rtn7VolWX3t6tVzZVeXkhKJbpaNTFduowmPAxzovxMeLWqecnZs9hLSlB6eKCpWBF1cDCSWo05ORlTbGw5Uc37iaXU8LakZzyQ8wsEgn83f+oJfMSIEYwYMeK6x+bOnVtu+8033+TNN9+85Zh9+/alb9/riF4JBHcJnVrJM80jOJWcT8sqvrSr6s/v5zPILDDi6azmf6XibCl5xXy58QK1lH15hi2y2nntx2D31/JArV6D1BOQcwkUKsi6AL8OKjPKTQUQtwsqd7hna7nyoHQFd427Qyhn9aXV1POvx4nME8TnxxOfXyYg1jWyK5+1/kx4xK+DJEm4d+1yy3Y+g5/FFBdH4LvvoNBoCJowAb9Ro1BflT5zZTz/V14hcZgcDeTRuzfu3bqSOPwFjBfkevPFR49SuGsn5vgEbEVFAOT9+us159SvXIlbt64416uH0ssbpzq1Ubq7o1+zBktqKj7PP4/daCS232NYc3KosHwZ2kqV/uolEQgE9wmDycLAWQc4FC+X1Jq5M5a9Y9sD0LKyLw/VCqJPg9L8bpdS8TJnHxrXrAr2KDizCmI2wNKh0HK0rE+idoFqPfh8wzkOx7bm6dYallxYwnmfIPok5cptAqLx1nnjbAeDJJGsUlEx47TD8LbarHxz9Bt6Vu5JBQ+5lNnVRjfIxjZqNZjN5DvDxZQ9NJ0/H3txMUp39zJxtasM7yth5k716yMpFEjOzjg3akTRnj0U7tjxQAxvu93u8HhbMoThLRAI7j7C9SX4T/Fc64rM2xNPfrGZin6ufNmvDu+sOMXb3avh46rlRFIe/1t+ipPJepbiQUuf+lQuOgJzuskDeIRBm7dAVRomXKKXRdjySqM8FCqwWeTcuntoeF+PK4b3+rj1jGk8hrr+dfm+4/ecyjpFYkEiay+vZX3ceip6VOSFutcvmyW4NS7NmlF500bHtqRQXGN0O9q2bo1Hz0cwJScT8PZYlG5uVFy1kpKz57AVFZExaRLGM3LEhXPDhqjDwijctg1VUCAuTZuh8vPDFBtL3pIlFKxbT8G69WWDS5JDa6Bw9x4UOh2WUpHKrKnfE/LlF/foCtycBxkmKhD8U3HWqNAXmx3b7ar5Of4deTpr+O6p+mWNXUs9zH7V5L+SBD2/hanN5PKYy4YCsE/Xgpij2ZxMzudgXC6VK6byW+pSUnXu9AHIkVOsJFMR4SYT57QaElUqKqafgUqy0b8tcRuzTs1iacxStvTbgkZZliJz5d+6pFCg9vPDnJLCFzHT2b1pJnO7zqVBgCz8dsXwvlpcrfjIYXndDcrW5dqmNUV79pA9YyZuHTth2L+P3F9/xf+113Bp3PivXeDbwJqTg90k11K3iFBzgUBwDxCGt+A/hVal5LnWZW/SW0f5sePNdo7tYE8n+jUMpX01f77aEsOj2cNZGrKQKtlb5QYtR5UZ3QA6D3h0JsztLn/v8D6sfhnOr4dun12TJ3cvaRTYCF8nX7KKs1h8fjFP13ialiEtaRnSEoAGAQ14f8/7TD0+FRe1CwOjBwKQachk4sGJxOfHM6HlBMLcw/jqyFdYbBbGNh4rjKi/gCRJBE+cWG6ftnJltJUrA+DSrCnpn05EGxWF30svIqnV1x3Hs/9j6Jcuw5KbgyUtnZJTp7CbzWirV8eSkYHxrGy8S87O2A0G8teuxXfEC/fd620tLCJxyBAknY7Qr6ag9PS8o/6mpGTy16zB66knUZY+rAsE/xU6VA8gJkP2Crer6n/jhj7yv2trUF1Scw2EejmDqz889StsGQ/xu7HZrEzMaoH6eAo1gtzZcSGTS3myoR3hFACcKqvakXKEMIuFc1oNCWq1bLyXsvCcHLLep0ofh9GtN+oZtXUU8fnxbOy7EZVCha52bcwpKcT7y/eLvSl7HYa30q1UXC03l+y5c5EkCcMh2fB2qt/AcS7Pvn3JW7ES49mzxPXpg1Uv57GnvjWWimt+Q+Hk9Kev7e1gTi2rrmMzGERpM4FAcNcRhrdAcBW+rloGNosEID2/hF8OJtIpeSgDnGowupEOr/qDrvXohTeBF/aCxkXOnVv3JugTIP00BNYsa1eQDumnIKguuPhwt1FICjqGd+SX878w8eBEHq/2eLl87kerPEp8fjyzT82msqds+OWV5NF7VW/0pUI9z254lnC3cGLzY/m2/beOdWYaMtEoNdeIvgn+GpqICMK+n3rLdk7R0ThFRzu2bSUlWPPyUAcGYoqLI2HwEMzp6YR88QV5y5ZSuHkLGZMnE/zRRxhjYshZ8BMqPz/8Ro26owdJa2HhDQ1gu9mM3WRC4VI2XvYPPzgEkxKeH0b47Nm3fT67zUbyqFGUnDqF8eJFQj7/7LbnKRD8G2hfzZ9p2y8B0KKy740b1h9EgcaPlr9ayd++lfPju6FRKSCkAQxcAaYiPl52kKPHinghwosgDx0KXRJni2Wl7gj3SHmcK4Z34n5CzXKZzUSVSr53IVfL2J+2HwmJx6o+5ji9m8aNM9lnMFgMxOnjqOxVmZDPJnJheF+S9supNemGMo+xotTwthUVkfFp2YtISaNBV7Psd03h4kLY91OJe6y/I9Rb4eaGOSWF7Bkz8Xt5JADWggJMly87qkjcLcypKeW2LRnpKF0fTK65QCD4dyIMb4HgBrzVrRqbzqSTXWQis2If3LrU45fDSXy89iytqviiUykxWm1U9HVhUPNIfF1L894qtpOF1/ZPg0rtIPGgLHxzxYugUENUF+j8kayWfhcZXmc42SXZ9Krc67oiaq82eJXOkZ2J9pEfdjx1nnSN7MqJzBMoJAWns0+TZ8zDVe2KxWbBZDUx7fg0Zp2aRahrKCt6rUCtKPPKphel4+PkIwTb7jMKnQ5FYCAAmshIKq5dgzU3F3VQEOrAAAo3b6Fw8xYubN5Srl/htm34jXwJXY0a2EqMmFNTcG7QAJVP+RdBdrud9I8/IXfBAjx69SLgnXfKGdB2m42EZwdTcu4c4XPn4lQzGlN8PDmlGh+SkxMlJ06QOHQoIZO+RB0cfN11GC/LYfQePR/BePEiJadOAZC/ejWevXvhcgdlKu+U4lOnyZ0/D6+BA8u91BAIHhSNIr1456HqBHk44aK9yW+qWodr3d4Yl63HbraRkldMpO9VL7g0LuxIkV+a1g/3QqdWYLe4OQ6H+ZSGqJeGmpN4gHCLHOaeoFbJZclsVhZfWAxA69DWBLuW/RtWSAqqelflaMZRzuacpbJXZSSNBinAz9HmbPZZx3eVjw/aqlUxxcfj3LgRkkaDKeYibt26ovhDdQd1YCBhM2eQPW0a7o88gr3ESPKoUWTPnIn7wz1QBwUR//QAjOfPEzLpS9y7d8eq12PNy0NTKvL7Z7kirObYzsh4YCJvAoHg34l4WhYIboCns4ZFw5qyPzaHvg1CKTJZmbz5AgUlFtaeTCvXdubOWAY0i+Dt7tWhajfZ8D46X/44kMAjFPSJcO43WaDt2bVw4he4vB0e/soRQvhn8XHyYVLbSTdtc8XovsLrDV9HqVBitBgZu3MsSYVJTGw9kSivKPYk72HGyRkAJBQksDVhK50jOwOwPXE7r2x9hQoeFfi2w7eEuIb8pbkL/jwKnQ5FaZ65rkYNAseNI2f+fEyXLiFpNLg/3APDvv2Yk5JIGfNWub6aChWIXPwr1qwssmfNwqluPax5eeQuWACAfsUKinbvRunni8rPj8B33qHk9BkMhw4BkPzKK4TPnkXaBx9gN5txadECv1GvkPDsYIqPHeNyz14EfzoBtw7lNQ/0v60h7b33sBkM5P78s6PkkCooCEtqKqkffEDFVatQaMuEnGwmk6yE7O5+R9fHmp+P3WhE4eqKwskJW1ERSS+PxJKSSv6mzYR+9RWurVre2UUXCO4ykiQxtNXtGXqSJBHq5czFjEIScw0EuOtw0igB0BebHSHr9cI9MVps5Q3voNLwbn0imEsg8QDtTMXU7P4BYcteBEsJJy6tZ/4Z+f51tbf7CrV9a3M04yj7UvfxcKWHAYjyimJjn410XtqZdEM6FpsFlUKFpFJRYcVysFqRVLd+7NRFRREySb6P2e12XJo3o2jPXhKGDMGpdh2M588DkDF5Ck4NGhL3+ONYs7KotH4d6pDbvw/ZrVYkpdKxfXWoOYg8b4FAcPeR7PZSdZ5/OPn5+Xh4eKDX63G/w4cygeB2yTOY2BGTRU6hEaPFhlIhsfp4CseT9HSuEcD0gQ3BWADLh0NBmiy25l8dKraVa307e8thfIsGyKroSg1YZTEXqnSRc/T+Znx28DNi9bHsSt5Fk6AmzOw8E7PVTM+VPUksSATAW+fN1I5TrzHqBQ8WS3Y2klaL0tUVa2Eh2T/8gOHgIYwXLiC5OGMvMWIrKMC5aVOMFy5gzckp19/ziccp3L4dS0qZJ0hbpTJ2qw3T5cugVILVWtZBpaLiiuVoK1fGlJBA8htvUHL8BKjVRPw4F6d69Sjcto3sWbMoLs3xVHp5Yc2VlZxVfn5UWLaU2D59sWRk4NHzEYI+/RRJkrAWFhH/xOOYEpMIm/oduho1SH1/HJJaTdAnHzs8ZyXnL5Dy+utoo6IIeOd/5C74iaxp0+R5ShIePXsiaTSyevwVgTqVitCvv8Ktffu7ev3/6felqVOn8vnnn5Oamkp0dDRTpkyhVatW1227bds22rVrd83+s2fPUq1aNcf20qVLeffdd7l06RKVKlXi448/pnfv3rc9p3/6Nb2bPDvnAFvPZ+KuU1FotPBWt2o837oS2y9kMmj2ASJ8nNn+RjtsNjs13l+PWXuasQ+FMbTuYzAhFEyF0G8uLH4GVE4wNhFmdYKUo0xo8hg/Z+wjxDWENb3XoFQoy537QOoBhmwcgrfOm9/7/e44brfbSSpIIsQtBIWkuCvrNGdkkDDoGUyxpR56SULh5oYtPx+lry/WrCwAgiZMwLN3r5uOZbfZyF+zhrylyzDs34/vC8Pxe/llAJJGvUrB+jIBS7/XRuP73HN3ZQ0CgeDfze3em4THWyC4AzydNTxSp3zY7JCWFdh7Kbss71vrBo//RInZyry9cbSs7EeN4Kv+EQZEw4DlMLsLFKSC1kN+AIrZAAn7ILzpfVzRrXmz0ZukFKbQdWlX9qfuJ04fh6fWk+re1Sm2FOPn5MfZnLO8uf1NlvdcXk71VvBguTqEXOnqiv9rr5U7Xnz8OPFPD8Cwbx8gG9XWwiIsqal49O1D4HvvYSsyUHz4EHarlbT3x2GMkesHKz08CPnmaxKfk0uZaatWJeDttx3CcZrwcCIXLCD5tdcp2LiRpJdGog4NpeTECfnkajW+zw3Fd/hwMr76Cv2SpQS89y4qPz+CJnxC4vPD0K9chTokBN+RI0n/5BPHuRNfGinnt1+Wc1SV7m4EvvcexsuxJAwejDU7G2NMDAWbN2M3GuXzlRrZ+hUrHOsP/e478n/7jfy1a0l+dTThc2aj8g+g5NRJXNu0uediTn9nFi1axKhRo5g6dSotWrTghx9+oFu3bpw5c4bw8PAb9jt//ny5hw4/v7Lw471799K/f3/Gjx9P7969Wb58OY899hi7du2iSZNb16kXlCfS1wXOZ5JfIudnN4jwAuBIaUmy+uHytkIhUcHXlTxDPer71Jf/LXhVgPSTsGuKPFildqBUy/enlKNUMltpEtSEgTUGXmN0A9QLqIer2pWckhxOZZ+ijl8d9EY97hp3wtzDrmn/V1D7+xP+41wSnnkW0+XL+L4wHJV/AGnjxjmMboDi48duanjb7XbSP/qI3J8XOvblLPgJ3+HDkTQaR6i5pkIFTLGxopa3QCC46wiPt0BwF7HZ7Oy9nM3xpDxyCk3M3BWLl7OaLa+1xdvlDwZpzmU4uRTqPA47PocjP0JoI6j1mFymLKAGhDW9J0Jsf4YXt7zIjqQdjKg7ghfqyOXI8kryUCqU9FzRk8ziTF6p/wpDaw296TiFpkJcNUKx+u9C7uLFpL0/DufGjQn99hsUOh2m2Fg0lStfo2hffOwY8QMGYjeb8X/jdXyGDKH49Gks6Rm4tmldLmzzCjaDgbinni5TXndywuvJJ/AeOAh1QJly8x9FC3MXLybt3fcAUIeHY05IAIUCbbWqjhJsSi8vrHl5YLfj2r49xYcPY9Xr0Vatit1kwhQbi6TREDhuHB69e1F85Agpb43FnJiIe48ehHzxOXaLhaSXRlK4bVu5eUcsmI9zw4Z/6dr+k+9LTZo0oX79+nz//feOfdWrV6dXr15MmDDhmvZXPN65ubl43kDNvn///uTn57Nu3TrHvq5du+Ll5cXChQuv2+eP/JOv6d0mJa+YGTsvEx3sQfUgN6KDZfHLU8l6Ri48yuAWkQwoFQs1W22olVd5oBcNgLOryrZ7T4c6/WHvVNgwFqr1gMd/uun5X9/+OhviNvBcred4qd5LNP25KSqFiiUPLymXE363sBUVUXL+Ak716oLFwuVevTFduoRrxw4Ubt6CtkZ1Ki5b5mhftGcP+jVrKNq7F6WLC6qAQIp27QJJwmf4MPKWLMGamUXotO9xa9uWmNZt5EibXr3Qr1iBW+fOhH791V1fh0Ag+PchPN4CwQMgMdfAUzP3X11imTZRfng5X6dMlHdFaPNGaaMxcPwXSDoof66gdYdn1kBQbTi9AgzZULMPOHne66Vcw2NRj7EjaQdtQts49nnq5HmMbjiasTvHsidlD0NqDrluCTKD2cCEAxPYkrCFNb3X4KXzul9TF9wEr379cOvQAaWXl+O/m7ZKleu2dapbl9Cp32E4fBivAQPkfdHRcBNxMoWzM2FTvyP1/ffRRETiO+x5VL7XKjb/8f8Zr379sOn1ZH77nWx0Az7PP4fP0OdIHv0q1jw9IZO+RL98BVnffUfh77/Lc4+KInzObBQ6HXnLl+PcoAG60lBn5wYNqLhiOYbDh3FpKkeWSCoVIZMnOXLSUavRRUU56vn+FzGZTBw+fJi33iqvB9C5c2f27Nlz07716tWjpKSEGjVq8M4775QLP9+7dy+vvvpqufZdunRhypQpd23u/yWCPZ14/+Fr/+1FB7tTyc+FHrXLjN9yRjfI959SLJKaBdnVeAYgsJa8M+XYLc/fMbwjhaZCoryjyDBkUGwpRiWpKDIX8dq217Bj59NWcqrIFVHOrOIstEotbhq3W4x+LQoXF5zr1ytdkJqIuXMwp6Sg8vfn4uYtGM9fwFZcjKTVkjnlK7KnTy9bIzgiZoI+Go9nnz7YCgrJXbCA/LVrcW3eHEtmJgBOdeugX7FC5HgLBIK7jjC8BYK7SISPCy0r+7Lrohz+1rteCJP71711R48Q6PQhHJoF3pXkmuAJeyEvHpY9Dw2flcuUAWz4HzR+DjqOg+uEAN4rWoe25u0mb6NVaq859lCFh3BSOtEuvJ3DgNqXuo/avrVxVjuz8NxCbHYb+1L3UWAq4MfTPzKqwaj7NnfBzVF5e992W9dWrXC9QZ7vjVAHBRF+1UPw7eIzdCie/fuTv2YttsICvAcNQlKry43l++IIkCRshYW4tGyJS6lqMoD3U09dM6bCxQXX1q3L73NyIvzHuZji4tBERpYTdPsvkpWVhdVqJSAgoNz+gIAA0tLSrtsnKCiI6dOn06BBA4xGI/Pnz6dDhw5s27aN1qXXOy0t7Y7GBDAajRivpAsgexUEN0eSJGYOanTzRldV1NhqqcW4jUl0qFuFsOB6ICkgPwm7PgnJI7Ssj90uh6mX0rVCV7pW6ArAnhT5hUyYexhapZaN8RtRSAqa/dwMXydfljyyhAJTAX1W9cHP2Y9ljyz7y9UwVH5+qPz8sNvtqPz8sGRmUnLqFLkLfyF/7VoAPPo8inu37tgKCzAcOIBz48a4d5Xn7N69G7kLFlC45XdMw5PAbkfSaNBWrQqAOfOvhZpb8/JIeH4Ybp06ilxxgUAACMNbILjrPN00nF0Xs/B11fL+wzUc+wtKzKTqS3DWKFl/Ko1e9ULKSpABNB0uf65QlAVTm0Hm2TKj2z0E8pNhz9dgyIFHvgHF3RGwuRWSJPFEtSdueKxDRJlqdVpRGi9teQlXtSvjW4xnxokZZBZn0qdKH5bGLOXncz/TtUJXqnlXu+54AsEVlG5ueD3e/4bHJYUCv5de/MvnUWi16EofuAUyf4xC+GM6wNVUrVqVqlddv2bNmpGYmMgXX3zhMLzvdEyACRMm8MEHH/yZ6QuuIinXwOuLj1NotPDbyFblPN5rrXJ+/U/7E3irWzU5zzvtJKO/nEHNNo8yxL4CLm6CjHPyPafutfeBWL0sfFbBvQJhbmF467zJKcnBZDeRUpTCovOLSC9Kp9BcSKG+kO1J2+kQ3uGacf4MkiThVLcOBZs2kzF5CsVHjsiiix+Nx6NnT0e7Kwb3FZzq1nVUUchbtAgAVWAg6tJSjZaMTOw2G9KfvMcW7thByYkTWFJTheEtEAgAuD9P7ALBf4gu0YF8/UQ9fnm+KZ7OsuftbGo+XSbvYMCs/XSdspOP1pzlqRn7yS8x33ggF1/o+W3Zdr0B8OppORdPUsCxBbD29bLjFmNZfPsDJsOQgb+zP9kl2YzYMoLM4kzcNe683eRtavvWpthSzJNrnmTGiRkkFyZfd4ys4iyKzEX3eeYCgcDX1xelUnmNJzojI+Maj/XNaNq0KTExMY7twMDAOx5z7Nix6PV6xycxMfG2zy8ow02nZt/lHE4l51NotIBvVZCUWJU6Ntvk8mKLDiZQYrZCmGyIR9vO43r4e9g1CdJOgs0Mm8fJJciuwmA28OmBTwGo4FEBSZL4rsN3vNfsPUbVHwXA/DPzy4WX/3r+7lbw0NWuDUDxkSMA+Dz/fDmj+3pICoXDGM+ZJ5dOUwcFyakwkgQWi6Piwp/BWPr/viUzE8tfGEcgEPx7EIa3QHCXkSSJR+oEU9m/TEAs1MsJSZJIzzdSaLSgkOB8egEvLDiMyWIr13/J4SSGzT/EjB2XifVuCb2+h/bvQI8p8sNAnf7w6AzZ+D40C44vgrO/wcRI2UN+ZiXYyo95v6ntV5sVPVfQo2IPx75OEZ3QKDV82+Fb2oa1xWwz8/XRr+m6tKvDW3KFbYnb6LKkCx0Xd2TWyVmUWEoQCAT3B41GQ4MGDdi0aVO5/Zs2baJ58+a3Pc7Ro0cJKq0vD7IX/I9jbty48aZjarVa3N3dy30Ed46Hk5oAdznC6mxqPrgFwBO/oBy4gmOf9CXE04lcg5nVx1MgtDEADRUXaG0o/e/VZgy4h0JhmvzS9yo2J2x2fI/0iASgpm9N+kX1Y1D0IEJdQ8kz5lHXvy5LHl4CyKHpCfkJd219TrXrOL6r/PzwGTL4tvr5PDcUXXS046W1OigISa1G6SuLmuZv2ED27Dl/SvPhSk45gOnixZu0FAgE/xVEqLlAcB9w06n56vG6vL/qNN1rBdGqii9PTN/HySQ9cdlFRAXInoBTyXreWnoCi83OhtPpfLr+HNMHdKBD6z94hGr1heyLsG0C/PYqWI1gs8hh6b8OlNXRu38BwXXv/2JL0Sg1fNLyE8Ldw1kXu46nqz8NgJfOi6/bfc2KiytYFrOMk1kncVWXvaR4Z9c7rLy0EgCTzcSUI1PYkrCF2V1mo1PpHshaBIL/GqNHj2bAgAE0bNiQZs2aMX36dBISEhg+XE6HGTt2LMnJycybNw+AKVOmEBkZSXR0NCaTiQULFrB06VKWLl3qGPOVV16hdevWTJw4kZ49e7Jy5Uo2b97Mrl27Hsga/2vUCvEkPT+dE0l6GkV6Q1RnAJTA000jmLb9EgHuOvCV88PrKi4BYNe6I7V8FZx9Yd0bcgmy+oPk8mPAwxUf5mz2WXYk7aBlSMty51QpVHzY4kP8nPwcRnmLkBbsTt7Nr+d/5fVGr3M3cKoZDUolWK34jRqFwtn5tvqpvL2J/GUhWd9PI3fxr7h16giA2s8fa2YW6R+Ol6+ByYTv8GF3NCfjVdEeJTExODe6Rd69QCD41yMMb4HgPtEw0ps1L5eJUk0f2JAAdy2V/WWj22Sx8cYS2ehuFOmF1WbHYLI6arNekwvZ+g2I3QHxu+Xtmn3ApzLs/U5WRp/eFiJbQuUOUG/gAylLJkkSL9R5wVF+7Or9vav0pneV3hSaCnFSyfWSz+WcY12sXGqoW4VutAhuwWcHP+Nk1klmn5rNiLojALDZbZzPOU8Vryp/WaBHIBBcS//+/cnOzubDDz8kNTWVmjVrsnbtWiIiIgBITU0lIaHMY2kymXj99ddJTk7GycmJ6Oho1qxZQ/fu3R1tmjdvzi+//MI777zDu+++S6VKlVi0aJGo4X2fqBPqweaz6ZxIygNAbzBTbLYS4K7lySbh7LmURXp+CVSpAC5+UCSrfOdV6IGX2gnqD4CdX4A+Eda8Bt0mglqO5hrTeAxjGo+57nkbBZY3OB+v+ji7k3ejuIv6JAoXF1zGjMKckoxHr5uHmP8RSa3G7+WR+L080rFPFRAAZ844tnMXLsRnyGAk9XUqlFwHW1ER5uSyNCrjhQt3NCeBQPDvRNTxFgj+JtR4bz0GkxUvZzWbRrfB21lDgdGCh5N8oz+bms+YpSf46vF6VPB1kTvpk2HJYLncWJcJoFRBfipsehdOLi4b3MkbunwMdZ6Qw9WLsiD5MFTudN/E2W6HAlMB3xz9BjeNGyPqjECpULIvdR+rLq7i3Wbv4qRyoshcxNidY9mauJVWIa34tsO3KKS/zxoEgiuI+9LdR1zTP8+28xk8M+cgFX1d+P31tvy0P57/LT/FQ7WC+O6p+uUbL3wSzq8BYF/bn2jQqju/n8ugvWED6jUvy218q8LAleBelk5gstgoNFrwdtHccB42u43N8ZvpFNHppsJ6d4LNbqPtorbkGfPY1n8b3rrbr9RwPfI3bSJr6vf4PPsM6Z99jjUri+Avv8DjoYduq3/xiRPEPVYmCunUoAGRPy24SQ+BQPBP5nbvTeJpVSD4G2C12bFY5Xdg43vVxNdVi0IhOYxugCMJuZxI0vPIt7uYszuWbeczmHvKxItOE/jFd6RsdIP8ENRnJrx8VA4394+G4hxY8QLsnya3+XUQ/PwYbBh784kVpEHhXyupcie4adx4u8nbjKw3EmVpqbSmQU35pNUnDq/4mstr2Jq4FYCdyTuZe3rufZufQCAQ/FOpHeoJwOWsIvTFZk6nyKXZwn2uE5YdJnup42wBnJCq8cnaswybf5j3k+rDU0vBNQCyzsOeb8r6WC0MnXeIphO2kJxXfMN5KCQFnSM7O4xug9nA8czjf2ltl/Muk2vMxY6dYxnH/tJYAO6dOlFx+TI8HnkEr8cfByB3/u0bzlfyu5W+vqXbMdyunyt75kwudetO7uLF2B+wXotAILi7CMNbIPgboJBgy2tt2Dy6DT1qB1+3Tftq/jSM8KKgxMIHq8/wzJyDjFt9hjUnUvli43n0xX9QSPeuKNf7HrYdWr4q79v+GVzYAPGlOZX7p8H+G9RXLs6Txdq+awIF6XdnoXeBC7kX8HPyY1CNQQB8feRrDqcfvm5bg9lATG7MdY8JBALBfwlvFw21QjxoVcUXvcHM6WQ9ANHB1/HO1B9EZlhXLjUaR7tqAczZHQfAz/sToEpHWewT4NQSsFlhxQjsn1WgKGYXJouNRQdL1ed3ToKN795Q8NNkNfHqtlcZvH4ws07OIqUwBYDckly+OPgFWcVZ1/TZnridt3e+jcFsKFubU5mH+0Lu3Q3r9nq8P5JaTfGxY6R/9jk2g+GWfa7kd7t17ABKJbb8fCwZt36JbYqLI2PKV5hiY0l79z0SnnkWW/GNX2IIBIJ/FiI5UiD4GyBJEmHeNxeDCfJw4ufnmjJ9xyUOxuWSpi/B101DodHK90/VL+cdL4dSDe3egbOrZUG2xc/I+70iITdOFsvZ+QV4hIExX/7bf76sjl6cI7fd9B48+sPdWu5fol1YO0bUHYGX1ouM4gzWxa7jpS0vMaPzDGr61sRsM3My8yQuahde2foKyYXJNA9uzmsNXyPKK+pBT18gEAgeGKtHyuJnJWYr59IKAIgO9ri2obM3fkMW0QGu8dTa7Xakyh3ByQsK02H3FDj2ExIwTTOFHsaP0RtMYMiBLaU12KO6QmSL687JSeXkENKccmQKGoUGO3bMNjMl1hLeafpOufYv/f4SAGHuYQ79EG+dN280fIPPD33Omewz15zjVtjtdpIKkygyF1HNu1q5YypfX7yHDCZ72g/kzJ5NwZbNRP78MyqfMt0Um8mEpFAgqeTHamOpirmuRg00kZGYLl3CeCEG9S3K8WVMngIWC5pKlTCnpmI4cID8devxfLT3Ha9JIBD8/RAeb4HgH4RGpeCl9lX4cXBjNrzamp+GNmXliy0I9nS6eUelCtq8JX83G0ChgoGroOmLclmywnRIPgRZF+DSFjg0B47/Utb/xC8Qt/veLewOaBHSAm+dN5IkMa7ZOBoGNMTHyQdfJzmkb9XFVQxaP4jHf3vcUSN8T8oe+q3ux6pLqx7k1AUCgeCBY7PZee3X4xgtNvzctETc4qWvJEmc/6irYzs5rxhUGoguNQa3fAiAHQk/Sc80zRRaVPSA5CNlgxz/+bpjawrS+aLJu7zX7D0aBjREQsJkM2G2manmXY0O4R2u6fNM9DMAJBUkldtfw6cGAGdzzjr22e12ZpyYwca4jTdd48b4jXRf1p0P93543eP+o0YR+v1UVAEBmOMTyJg82XHMnJHB5a7duNyzF3azHHl2xeOtrVIFbZUq8r5bCKwVHz9OwYYNIEmETJqEz7PPAlDw+5ab9hMIBP8chMdbIPgXseF0Ggdjc3inRw1KzFbWn0pjz6Usqga681B0dwJ9q8p5ebUfB68I6PoJtP8fpJ+Wc7nTTsD2ibIHvDgXkKBqd1lk55cnoOpDsrJtxO3X8r2XOKud+a7DdxSZi/Bz9gMgozgDlaTCYrfQJLAJrzZ4lVmnZrE3ZS+NAxvfcKzzOedZdH4REhLdKnSjYWDD+7UMgUAguG/8tD+eNSdTUSslvnq8LgrF9QXObDY7RxJyic820LNuMDWC3DmTms+ZlHxCvZyhdn84NFtuLCnZ3mgq9fe/Sj3FRdCehZRjZYOdXgndPgfNVUZ+7A6Y1wuVUkO/Wn3o1/p9ilz90Bv1mG1mwtzCUEgK7HY7BeYC3DVySHy0T7TcXR8LgN6oZ2P8Rqp4ygZuWlEaOSU5eOu8OZl1kq+Pfg3A/pD9OKuvfcmQWpjqiIa6kHsBi81y3WoZbu3aofTwJP7JJ9EvXYbX40+gq16NlDfexJwih8gXbNuGS5MmWNLl9Cxt5cpoo6pQsH49BVt/x61LZzShodeMXbR3L8mvvwGAR69e6KpGgdVC1nffUbR7D7aSEhQ6HXa7nYzPv8BeUkLA2LduW2VdIBD8PRCGt0DwLyEuq4gXFhzGZoct5zJI05dQbLY6jlf0dSGwzww4PJcN/oM5uu4cZqsNfzctA5s1wClMCVU6w7GFoC8tE1ShFfT8FmbHyN7w4z/Ln+je0Gk8eIY9oNWW4ax2Lvcw9UKdF3i44sOcyzlHm7A2qBVqJrWdRFpRGoEugY52b+18i+SCZLQqLYWmQk5nn3YcC3MLcxje53LOsfLiSs7lnCMmL4ahNYfyTM1n7tv6BAKB4G5hstj4cW88AF/0q0PzSr43bf/kzP2YLDYaV/CmUaQXTholKmWpoR7WBDzDIS8B6j5JSXgbDp5qQwfDOri8DXIuX3XiAjj3G9R+TN62WmDdGLBbwVIMRxdAfiouA5bhonZxdDuSfoR3d7+Lv7M/c7rOwW63U92nOiC/LDXbzBxJP8KHez+kkkcl+lTpg6+TryM8vpZvLTy1nuQZ89ibspcOEeU96BabhQHrBjjEO41WIwn5CVT0rHjd6+Fcvx7ujzxM/qrVpIwZgyYyEsP+/Y7jeUuWYM3NBUAdEoLSzQ2nOnUAKD50mEudOhP44Qd49evn6KNf/RspY8aAzYa2WjX8XxsNgLZ6dVRBQVhSUynauxe3du0o3L6dnNnyyw6byUjQ+PF3TRleIBDce0SouUDwLyHS14VXO8pv7WOziig2Wwn1cmJIywq0ifKjaUUfCKoDPSaz6qKZadsvMWtXLBPWnWPArP3M3xvHR+svQqtXywat/Tg4e8MLe2HQb1B/oByafno5fFNffnAqyn5AK74xoW6hdIzoiFpR5g242ug+lHaINZfXcCzzGPtT93M6+zQKSUG3yG4MqDGA5iFlHv2z2WdZcHYBh9IPoTfq+fLwl8w/M/+25pFelH7bSrYCgUBwr9GoFHzUqybzhzSmZ92Qm7ZVKCTCvGSDtMc3u3iudUWWvtCc9tXkPGWj1SZXzoh+FDq8R9eaQXToLhvWJec3Y00qFb2s2A4A27GFfLL2LPP3xcORuZBxBnSe0HOq3C75EPzh9zLQJZCEggQOpx/mct5l2v3ajqnHpqJRaDDZTFzOu8zRzKMA1PWvy7jm43ip3kv4OMn515Ik8UilRwD4PfH3a9b409mfSDekk2/Kp7p3qUGfe/6m18X/tdeRnJ0xXbpE4RY5DNz3xRcBKNq5i8wpXwHgPXAAAK4tWhA67XucmzQBu52s76Y61MptBgPpEyeCzYZHz55E/rIQVakSuiRJuLWTr13h779jt9nI/Oprxzz0S5aS/cP1xVENR49yqUcPcn766aZrEQgE9xfh8RYI/kW81L4yDSK8QAJ/Nx0VfV2uG0bYsbo//m5a1EoFCw8kcCg+l0Px8lv6lgM70dY/GowFUEN+YEGpkr3fFVpBo+dgw9sQt1NWRb/0OwzeIAvpHJ4jG+d+Ve/nsu+YKO8ofuz6I9kl2ZisJrRKLVW9qxLmdq0Hv4JHBQbWGEgFjwokFiQy+9RsPjv4Gb8n/M7TNZ525CAazAacVE5IkkSRuYjdybv5YO8HPFn9SV6s++L9XqJAIBBcl6YVfW7dqBQ3nfzyUl9sxk1b9iLziw3nmbnrMu8/HM0T/eaUdajQBhsSulLj1SYpMXcYj/ZyS7i8lWVnjmKU1Dzp8RFKgHZvQ61+8NsoKNFDXjyodLD1Y9B5EBxQkzq+tTiedZLBGwaTXZLNuZxz1ParzaH0Q5zNOesoH1bXv+5119A+vD3zzsxjW+I2Rxi5zWbjne2TWJ3wIwCDogeRUpjC2ZyznMs5R7cK3W54TdQB/kTMnkXh7t3Yi4vRVKqMZ+9eGPbvx3DoENacHNQR4Xg98UTZdWzbFpdmzYhp2QpLWhqGg4dwadKY3J9/xpqVhTo0lKDxHyJpytc/d+3Qntyff6bg962ow8Mxnj2LwsUFn+eGkjnlKzK/+Qb3Hg+hCQ3FWliEpFJiSkggcdhwbPn5ZH45Cffu3VF5ed3Wf2+BQHBvEYa3QPAvQpIkmle+eeggQO96ofSuF1r6PYRBsw+QUWDk5faVaV0tFKK2YbPbUai113YOqg2DVkPsdlgxQg5B//ERWSHdVABnV8HwXaC7jlLu3wR3jTv1A+rfVtu6/nUdD3R2ux2lpGTGyRkcSj9Ez8o9AUjMT2TElhE0CGhAuiGdfSn7sNgtAOxN2ctztZ4jpTCFN3e8ST3/evSJ6iMU1gUCwd+enCKT47uHs2x4b7+QybdbZdXut5efxMtZTZfoQErMNpxcfMh1r4ZPvixwdt4awvFkD2rYKlJbcZmOStkLrizJBe9K0HCwXHnDvwakHoPUE5CwD47Mc5z35UYDGaV2I7tEjq5qE9oGi93CofRD7Eja4TC8GwQ0ACDTkElMbgw6lY5JhyfRPrw9Xlovco25HEk/QuOgxnyy8yeH0f1CnRd4NvpZlsQskeecc3OPN4BT3bo41a1bbp9H3z4YDh0CwP+1164xohVaLW5dOqNfspT831aji65B9oyZgOwx/2N7AJdGjVC4uWHNzibzy0kAeA9+Ft/hwzEcOkzRrl1kz5yJZ58+xA96BntxMZJajd0k/3ezGQzkzJuH/yuv3HJNt0PuL4tQenrg3rVMbM9ut1OwYQNOdeqgDgq6K+cRCP6tCMNbIPiPUz3InU2j25BZUEJlfzfOpxXwxcbzBLhr+ahXret3kiSo2BaeWgKzu0L6ydL9Cjnfb/UoqNwR0k/JeeMV28p9rmC3w95vZaO9yyegdbvHq7w7SJLEy/VfpmflnuxP3U+LYLk8zqH0Q8TlxxGXH+doG+IaQruwdrxc/2XUCjXv7XmPszlnOZtzliUXljCl3RQqeFTghxM/0DiwMQ9XetjR12qzsjtlN/X96+Oqcb3fyxQIBAIARravzBtLTvBofTks/bEf9nIgVi4zGeCuxW6X05yyCk00+ngzIZ5OLKncBk7JhneMqgoalYL11kbUVlzmWa+T5OXL9cNzqvbHW6nmUmYhCflBtOMY9tTjSJdKQ8JDGkLyIRrHbGfNsG1MPzmD09mn6e9TH13qcV54bAcfHPgYO3Y6RXQizC2MAlMBHZd0xGa3Ud+/PsczjxPsEkzbsLYsv7icTfGbqOtfl+Wxs0ACY2YHhtV+AUmSqOYllxE7l3Ou3DXIKs4iuTCZOn51HPsyDZnojXoqe1V27HPv2hX9ypWo/QNw69TputfTo8fDsuG9fgPWvDysej2aChXweLjHddtLGg3BEyeS9+uvFJ8+hcrHF+9BgwDwHfY8Rbt2oV+6jMIdO7CX1he3m0xoq1TGe9AgUt95l9z5C9BGRmKKj8etSxd0VctHpNnt9tvKEzfGxJA2bpz8feQlfEeMQJIk8n5dTNr77+NUpw6Ri365+SACwX8cYXgLBAI8nNSOOuB5BhObzsiKrMm5xYxoV5lGkd7X7xhQA574Gda8JtdpjeoCc3vA6WXyB2DfVPCpLNcNdw+Guk/LIjt7SnPVirKh/wJQ/HMkJyLcI4hwj3Bs967Sm7SiNH67/BsdIzrSu3JvIj0iy/WZ1XkWe1P3Mv/MfPal7uOVra+gUqiIdI9kWO1h5dp+ffRrZp+aTaeITkxqO+l+LEkgEAiuoW+DUCr7u1I9SFYUN5gsjmPrX2mNyWojwF3HoTjZGJck8KrVCU5NA8C7SlNa1Avhp5R+cGgRUYbDSAoLdiQ8mjxFen4JA2cdoF1BIO3UkHpwBcElFwEJHvsRvmkIefF45cYzpvEYyL4EMztAcS4xxdlsTJLLhA13rgSAm8aNAdUH8OOZHzmSIZczaxjYkADnAJZfXI6H1oPFFxZjkrKxmd3pV3kgytJ0rMpelRlScwjVvKths9tQSAr2p+7nhc0vEOgSyOpeq1EqlNjsNgauG0hSYRJPVnuSVxu8ik6lQ6HTETHnqrD76+DcuBGqwEAsaWkUbNoMajUBb4911P++Hm7t2+HWvt01+50aNsSpfn2KjxzBkpKKOjSU8NmzsObkoKlcGYWzMznz5mO8cIGUMXI50azpM/B7+WW8nnwSW76e1PfHUXziBBFz56CrXv2mcy/aVyYil/XNt9hLjPiNeoXs2bMAuRxayblz6KpVu9EQAsF/nn/Ok65AILgvNK7gzdNNw5Ek2Ho+k/4/7GXZkaTrtj2emIchuCm8uB86fSCXGev0AQB2v2oUVu0LahfIvggXN8vhg7M7lxndCrVcqmzrx/drefeMF+q+wJpH1/Bqg1evMboB1Eo1rUNbM7XjVDpFdMJsM1NsKcZd646bpszjfzrrNHNPzwVgU/wmLuVduq3z2+12Rv4+kh7Le5BVnHU3liQQCP7jSJJEvXAvdGolAK91rkoVf1d+GtoELxcNAe46AOKyZW9rpI8LuootMUtymlKDFp2QJImne3QCv2pINtlwlyq0QukZygerT5OcV0yyTk69kY1uILgueIRCVGd5+/QKWUfkp36lpS4h4cQC3FROdDKUELX2bTlEHXilwSvU9avrWEPjwMa0CmzMJy41eFHhT6eITrib2mDM7ETLSmWh0U4qJ0Y1GEXXCl1RSPLjcS3fWjirnUksSGRLgiykdjbnLEmF8j3x53M/M2j9IMw28+1dT4XC4d1WenoSMXsWrq1a3Vbfa8aSJHyHPS9vqNWETJ6EJjwcp7p1Ubq6IikU+L/xOqhU5AY4cypcAouFzEmTuNCkCZe6P0TRzp3Y9HpyFiy45fkMB2TDW1e7NgDZM2aQMnYs5vgER5u8JUv/1FoEgv8KwvAWCATlkCSJj3rVYutrbelROwibHV5bfJyp2y5SZCzzdszceZme3+2m57e7KbxqP81HwluJfFZxLjWPP8qMhqvh8YWycm29p0FZmjfe7XN45Bv5+84v4Pz6sjHsdjixGBLK3rBjLLxG8fafiFqh5rPWnzGq/ig+bP4h0ztNx0Mr58OP2jqKV7a+gs1uQ6VQ4a3zJrkwuVx/q83K3FNzGbxhMB/t+4jfE37HbrezJnYN2xK3EZ8fz6RDd+4lLzAVsOTCEjINmXdjmQKB4F9Iu6r+bBrdhhZXaYnoDWbeXi6nG0X4OINah/rxH6H7FziF1yvrXL0snYbajwMwvmdNOlYP4MPn+2OTlI7DBSGt5S/RveW/p5bCvJ6Qcwk8wsCvOh302Wy4FMOYrNLKGqeWUVBixmpV8HmbzwlxDaGuX10i3CNQnFvDw6fWI214C38nP4rTemLRNyLc59q63leYf2Y+005Mo3FgYwDmnJLLme1I2gFAVa+quKndOJN9hgOpB277GvoMG0bA2LeosHQJzo0a3bSt2WZmyuEpXNZfvu5xl9atCfr4I8J/mIZTrWtTw1xbtaLa8WMMG2ziwycV/NBDjTo8HKxW7CUlaCLkyK2CjZuwmUzX9L+C3WbDcOAgAIFvj8VnmByllb9qNQBODeT8ev2qVdhKSm5xBQSC/y4i1FwgEFyXSF8Xvn68Hj4uGn7cG89n688T4KajT4NQjiXm8ek6OQ8uJqOQ1349RusoP55sHI4kSejtTo5asRO2pVHruaY0reYD9Z6Cjh+CIatM+Tz1mKyOvmK4LMrmFgxrX4NDs0GpgaGbQZ8ES4ZAlY7w2PyyfPH007D5A2g7BkIaPICr9OdQKVQMqTWk3L6E/ASHR8VL68XMLjMJdwtHp9JxMvMkC88txEPrwZnsM44QyoNpB1l0fhHzu81n5cWVjrFWX17No1UeddQivx2OpB9h/L7xzHKZxfzu8/F1urVIn0AgEOyIycRkkctjBXvK5ceoeh1V8OqPwI7PQe3sMMJ9XLXMHFT6O+VXVS4xBhxR1aUNyBohKifQJ4I+EbuzLxkPzyOAXFjwKK42K65qZ7AayDm0mAY7WjFzUGM6RPnyW4P3ULoHyfnLV/LGDdkUpl92iMZtOpPO11ti+OaJ+mhUsi/qbPZZ5p6ey8mskyQWJPJ2k7fZkbSDU9mn2Je6j51JOwF4otoTuKhd8HHyob5/ebHOS3mXqORZ6brXS+nq6sjTvhW/J/zOrFOzWHlpJZv6bkKlKP/YLkkSnn363HQMSamkhk8NzmSfoefLX1E5vB2mpGQsmRk41azJxQ4dsWRkULRzJ67t2mFOSMCSm4skSagjIlB5eWGMicGq1yM5O6OLjkZXsybFx45h2L8fSasldMpkYvv3x5KSStq4D9BVr4Zbt26o/f1va50CwX8FYXgLBIIbolBIjHskmioBbiw9ksRDteWwvF0xmVhsdhpHenM0MZcNp9PZcDqdADcdHWsEsPhQIgaTFUmC1lF+RAVcJZ7m4iN/rtDpQ0jcDylHZaE21wC5niuA1QSLnoaiLLAUw9nVcHwh1H0SbDZZVT31GFhKYNCq+3dh7gEhriF83/F7tidup3vF7uVUz002E6svr3ZsO6ucea72cyQXJhPmFkZd/7pM7TiVFRdXcDzjOCsvreTlrS/Tv2p/XqlfpmZ7MO0gDQMaIkkS+aZ8vjnyDXX869CjYg+aBzfHReVCUmESIzaP4Jv23xDgEuDoezj9MFsTtjK01lA8dZ7Y7DYMZoMQfxMI/uN0rRno+F7J7ya/B0G1od9ccPEDnfu1xwNrQ8YZbCon2nR4SN6ncZGN+NPLSFOH0S93NEFbjPw6rAPU6AWXt8KTv1LyYx+8rTk0UZzDf/8WWLUSVXEOOPvAK8fh4hbHaayJh+hWszK5BhM/7okj12DmRFIeDSO9sdvtjN05lkt6OcVHQqJ7he7E6eP4+dzPTD48mabBTTGYDbQKbYW/87WG5f7U/YzYPIKNfTfi4+SD3W4nw5CBwWLgm6PfkG/Kx0nlxJCaQ8qVQMswZLAnZQ89KvZwGNiLzi8C4NEqj15jdN8JSQVyaHyom1zNRBMagiZUFsxz796dnLlzyZm/gMxvv8N49my5vrqaNXFpIQuJOtevj6SW9WBCvvyC1PfH4dqqJSo/Pzz79CHrm2/Rr1iBfgVk/TCd4M8+w7Vliz89b4Hg34YwvAUCwU2RJImnm0bwdNMyMbGX2lehWqA7jSt6s+ZEKm8vP0kFXxe0agUWq405u+MA+OCRaJ5uEnHdWuIOVFroOwdmtHN4NZAU0HUi7J4iq6SDbJAXpsP6sbJi+qXfZaMbIHYH6JPBI+SeXIP7gVKhpGVIS1qGtLzmWDXvaoysN5ICUwFWu5Unqj5BmHv5muNqhZp+Uf3oHNGZXcm7yC7JJsOQ4Ti+J3kPwzYPo0VICz5r/Rlv7niT3cm78dLJ9V3VSjW/9PiFAesGcDbnLJ2WdKJpUFPeavwWFT0rklWcxa8XfuVU9inGNx/PS7+/RGZxJgu6LaCiZ8Wbrm32qdl0DO9IuHv4XbhSAoHg74RaqWDVSy3YdzmbzjUCbt74Suj49QhtCCd+QVGxrXxfuELXCSzOCmN8fDT5uJKXko/NZkfRby7YLMRklXDcVJ++yh18r56CV1xhWV9DNmx8FwrTHLs8ck7w/dP9ARjx02HWnkxjz6VsGkZ6I0kSz9d+njE7xwBQ07cmHloPhtUZxqpLqzibc5YBNQYwusHo6y7BarPy+cHPMdlMrI1dS4fwDry18y3SitKw2CxkFpel8gyuOdjxPa8kj0HrBpFUmERRbixP2V25VLktB9MOopSUPFzxYdZeXkukRyQ1fGrc/Br/Ab1RT74pX77EbqEO4bgruD/0EDlz52LYJ+fISxoNKn9/7BYLlrQ0Sk6douTUKQCcGzd29FP5+hL23beObZ9nnwWbHUt2FsWHj2CMiSHxuefwfuYZ/F4eicJJjoYo2LKFgk2b8Rn2POrgYDInTcJ4ORa/kS/hVJo/LhD8WxGGt0Ag+FN0LH3AeqJxOB2rB+DtokGpkNhwOo3kvGIAHmsYVs7oTskrxsdVw8bT6czceZkCo4UFQ5oQ7F0BXjoMifugIFUOGw+uB4E1YUEf8KkEA1fBvEcg7SRMbwdWozyoUiN7xk8uhpajZAGenFhQKCGoTvkyZv9QXNQuPF/7+dtq66H1YMkjS7icd7lcuHi6IR2dUsfu5N10W9rN8SBW1austEy4ezjTO03n4/0fczTjKHtT9zJg3QC+6/AdVTyrUGwp5nD6YXqu7OkQE5pzeg7/a/I//rfrfxitRj5o/gE+TmURDZviNzH58GSmHZ/G6w1fp0+VPigVZbmc95Ls4mw8tZ737XwCwX+V2qGe1A71/GuD1B8EdhtUk73d+y5ns+hgIh/1qsmHaS0oQNYSKTBaSMw1EOHjAko1q0/EcsLahL7KHXhJhVhRoOz1nSzCtuFtOFyqNH7lXpF8xHHKZpV8WXsyjZ0xmbzcoQoAXSK7MO3ENGL1sTQLbgaAt86bIbWGMO34NPKMeeWmHauPZeG5heiUOip4VOB87nnc1G70qNgDrVJLcmGy4yVoFa8qPBv9LAazwRHVZLaZeWj5Q47f5FWn5/NU3CUWNe4LQNuwtvx87mcWnltIz0o9+ajlR3d0Wa8IwQF0XdqV0Q1G07NyT8c+Xc1oNJGRmOLi0EVHE/rdt6gD5SgGU1wccU89jTVbzqN3aXzjnHSFszN+I18CwGY0kv7JBPIWLSJnzhwKft9C8EcfYbdYSHplFFgs5G/ciCYkBGNMDABFu3bhNeBpAsaOdZQ3s9vt5C1ZgtLNDbfOnZH+UP3Ekp1N5pQpePZ/HKea0Xd0XQSCB4EQVxMIBH8ZPzetoyTLqWS5RuvrnaMcSrjFJiujFx2j+ae/U/Wd9YxceJTjSXouZxYxZukJ7HY7uPiQHdoRe8MhstENskr6q6fh+e3g7A29vpfDFPOToChTFtnpNF5ue3whrHwJPqsAM9vD9DawZrQckv4fw9fJl8ZBjct5ontX6c3cbnPx0fk4HvAmtppIh4gO5fpW9a7KvG7zWNt7LbX9alNgKiCtKI2KnhX5rsN3SEiYbWYkJJ6o9gRvN3mbt3e9zcb4jWxP2s4z658hpTDFMV5Nn5o0CGhAsaWY8fvG89Tap9idvNuhvH4p7xIf7fuITEMmFpuFTfGbMFnLRH4u5l5kecxyLuReuOW69yTv4XKeLEJ0IvME7Re355n1z1BgKiA+P/6G/S7lXeJ/u/53jZCdQCC4j6g00GQYeIRSUGJm2PzDLD+aTPNPf6fAaCHAXUt0sByifjpF/g0rNFpYciiR3bZa6JU+WO0Sn7u8Jqcj1RsAV1WMoO5TANhTj2GzmCHrIu0qyccPx+eSZ5B/d5QKJRN9W9Cv2EpwWiDp+bJY2NPVn+a33r8xoMaActPONGSy8NxC5pyew3t73gPg+drP46XzwlntzBsN3wCgsmdlZnaeycOVHqZ/tf64qF0A+Pbot+Sb8lEpVKgkJWckM/t0WpZlyilX/av2p2tkVwDWXF5Dv9X9mHhgIsWW4tu6rEpJSdvQtgDklOQQq48td1ySJEImfYn/W2OI+GmBw+gG0ERGEj5jOgp3d1RBQeiib8+4VWi1BH0wjtBp36MKCMAcn0D8gIEkvjACLBaUXl7YDQaMMTEoPT1x69IF7HZy5813eN4BMidNJu3d90ge9SqxfftSfPx4ufNkT59O3uIlJL/8Mrbia69H5tdfc6FJUwp3776ted+KknPnsN9EhE4guBWS3f4vkAkG8vPz8fDwQK/X4+5+ndwhgUBwX7Db7ZxPLyDK383h7bba7DwxYx8HYuVarz4uGvo2COXHvXHY7LBiRAuqB7nRa+oejGYrr3aKokt04PVPYC6WS8vEbIAmw2VBni+iZE/GFdyCoCANsEPt/vDwV6B2gpJ8iNkIF9bLoYydPwInOdSagjRY96Yc0t7qNXC7zvktJvnh8B9MUkESnx/8nMZBjXmq+lM3bWswG9ifup924WU1ZNfFrmN/6n5G1huJj5MPNruNKUemMP/MfLx13mQYMnBSOVFsKWbpI0uJ8orCarOy8NxCvjv2HYXmsjDQwTUHcyjtECeyTjC8znDOZZ9jW9I2fuj0A82DmwOykvCkw7JK+9BaQ3m25rOsvbwWCYl+Vfs5QibXxa7jzR1v4uvky8Y+Gxm/bzzLLy4HwM/JD71Rz3cdv6NpUNNr1jlq66j/s3ff0VGUXQCHf7ub3U1PSO+F3nvvvRcpioACKlZQsH52QezYUCkKCIIKCFIEBaT33mvoJJBGEtKTzZb5/hjYEBIglBjQ+5zD0czOzL4zm+yd+1ZWR6+mQ3gHvmj1BWti1tA2tK291eV2SVy6++Se/nesi0rklXn7ScpUv9sHNwknz2Jjzs4Yhrcpx6udKvPyb/v5fc95gj2dmNLTj5GzNhNviODA6I7q3+9fr8GO79UTDt8BU9pBXga/2jowULsSvCvwhGkU65LcmdgziE5N6oHNAl9Vg8wE/rQ25BVeZtfb7XExFt1J1GqzMnz1cLbGbcWm2Ij0iGR+j/kYdPmx4lz6OYJcgtDr9IWOn3tsLrOOzuK1Bq/x277JrE8+aH+tjl8dfur8EwoK/Zf251jKMftrVbyqML7NeAJdAwudsyi/HP2FT3Z8QtvQtoxvO75Yx9ivMS0NtFrmnl+Cn7Mf7cPbF//YjAwSPxtH6rx5ADjVrUvYtKlc+nU2uUeO4PfSi+iDg4kbM4bU2XNw69SJkPFfk/LrryS8r1asa52dsWVno3V3p+zSJej9/FCsVk62boPlotp93+e5Z/F94QX7++ZGHedM795gs6F1cSH8119wrFSpcAGLKW3xYmL/9zrODRsSNnUKGsP9/Swg7q7ixiZJvIUQ/4icPCvHEzII9HTE19WIRqPhzwNxVPB3paK/GzEp2XT6egPZeVYAPnigeoFx5Tf022A4shgcPeDBn6BcGzg4HxY8BYoVykSos+Pumw15GfnHeYZB50/UpHzx82pLOqiz7tYdDLUehsDaanf1I3/AvCFqUt727bt6b/4NotOjMegMjFw7kiPJ6szEBq2BlQ+uxMvRC4CknCSmHpzKHyf/IMOcQaPARvQu35vXN76OBg0KCg4aBz5r9RkdwjsA8Nfpv/j9xO/siFeX69FpdFgV9Y4/bmMAAQAASURBVHfkkSqP8L+G/yM+K54+f/Qh4/Jn+0GzD/h0x6dkmDNw1DmSa1VbrF5v+DqDqgxia+xWph6ciqODI75Ovvx+4ne0Gi2/9/id45eOU82nGuHuxfzduwGJS3ef3NP/llyzlfm7z7M3OpX/da7EisPxvLP4MK0r+dKnbggvzN6LVgNznmpCjWAPqryrLku5790OeDobIPkUTG6uDld6eiP81APObizwHmatEbMVnDUmaPW6OsRp7iMAmBQH3q2wkE8faUlKVh5eLtckWymnwWICvyqYbWYSY3fj5RqEk+ftzWcRtfoddNsmscnJkR/KePBdpx+pG9RILafVzOm005xMPcmnOz7lkukSoW6hLHlgSbGG02yJ3cLTK58m0iOSPx649clI91/czyN/qfflwOADt1wxmbVtG1nbtuE1ZAgOZcoUej03KoozvR4ABweCPvqQ2NffAJsNnxeep8yAAcQ8MYzcI0dw69Ce4G++IXvHTqKHDAEHB7BY0Oj1ODdujCUhAc++fchYt47srdvQ6PUoZjMO/v6Efj8Zx8qVr1tG0+kzKOY8jBUrFrg+xWzmVOcumC+ovaI8+vYh8IMP7rhyVvx7SOIthLjvpGWbGff3MX7epk6o1qKCD5ey8/BxNTLjsYbXP/DSWXX5sbpD1AesK06uUhPqjPyuz3iXh0pd4egf6nFX8y4Pjp75s6qDuvZ41y/gu/rqxG8A/X6E6jdewuW/SlEU9l/cz4qzK6jqXZUe5XoU2ifHksP+i/up5VsLB40DHX/vaO96Pq7lODpHdi50zF+n/+K9Le+Ra80lzC2MTHMm0zpOo3yZ8ny791t+OPADeq0es81Mec/y1PGrw4GLB/iw+Yd8u/dbOkV0oke5HmyN3cqI1SPIsxXsLng7YydvRuLS3Sf39L9t97lL9J20BReDDr2DltRsMy+0Lc9LHdWWzBG/7sHDSc+LHSri43p5gra0C+rs6E6eWFa8i8NWtbU3L6I1BqwFE3GNrsCyZgCHa79H4qm91MxYj3ulVuir9VS//2O2q2uL2yyXV9XQqD+7B8ELe9V5Roojdi9snahW6C5/A6L+BCBbo8H5iVXqcpvnd8GjC8ErUj0kM5ZRa0fRPLg5j4V0xMnZFwdX7yJPn2XOwtnBmYTsBDrM74CDxoEdj+wgOj2aC5kXaBnS0r7v5P2T2RW/i3Gtxtkn3rxi6sGpjN8zntahrfm27bfFu7ZbdHbAQHL27rX/7NG7N4EffYhGo1ET8779wGIhaNxnZO/aTercuXj07YMlLp6sLVsKnU9jMBAxZzYXXn2NvFOnQK+nzEMP2SeOMycmonN3x6NnD7J37iJt0SIA9EFBePbvj/fjj6HR60n9/Xfi3nobrZsbtqwssNnwe+VlvIcNK/SepjNnMJ08iVu7doXGpIt/rxJNvCdOnMi4ceOIi4ujWrVqfP3117Ro0aLIfRcsWMCkSZPYt28fJpOJatWqMXr0aDp16mTfZ8aMGTz22GOFjs3JycHR0bFYZZJgLMS/g6IofLzsGD9sOG3fVjnAjeWj1IeDuLQc1kdd5ExyFu6OekK9nGlb2Q/X63QDxJQB6z+Di8eg/hNqy7dWCzmpsPIdiNmhTsgW2hB6fqt2PT+5Sh0zfniR2mIe1hSit6gPZYpVbRF/YqXaMmKzQfoF8Awt+v3FTc06MovPdn7G8NrDeabWM9fd73zGeRKyE6jjV4ccS459jKSiKMyNmkukRyRPrXwKm2Jjca/FRHpEFmqRMFvNrIlZQ7Y5G7PNzLn0c2SZs+xd5+8miUt3n9zT/7bsPAtvLzxE1SB3qgd78PO2c3zdvzYOuhsnOFkmC8sPxeMdu4bWu5/nouKJz6u70DiVwRqzkzyDO06bPlV7Tl0209KBwQ4rUfTOaMzZBU8Y0gAl6QSa3FQAFFd/NIpNnXsE4PEVENYY8rLAwbHIJFxRFPX7aVondWLRip3VBDs7KX8Vj1oDYf+v6gEBNdS4o3eynyPz1HYMs7oS7xDEtn5vU8m7MrV8axV4n07zO5Gel86PnX5kyPIh5FhyWPzAYsZsGcOexD20D2vPRy0+wmKz0HS2OsSnR9kefNTiowLnefLvJ9kWt83ec6gkXOnODeBYtSrhv/6C9qo84OI335I0cSJoNGgMBhSTibAfp2EoX56UH6ejDwkBm42L336LLSMD72efwW/kSCwpKcS98y6Zq1df761VGg0aoxElN9deBs+HHiR56jTM58/j99praAwGEj74ADQagr/6CltWFpnr12OIjETJzSHll1/BYsH7mafxGzXqum9lSU5GYzCgc3O77j7i/lFiiffcuXN59NFHmThxIs2aNeP7779n6tSpHDlyhLCwwl1rRo0aRVBQEG3atMHT05Pp06fz+eefs337durUUSdQmjFjBiNHjiQqKqrAsQEB1xnjWQQJxkL8eyiKwuqjiVxIzSHY0wk3RwcalVWTor8Px/PUrN0F9vd2MTC8TXlqhXpQNdCDpEwTc3ZG4+ig4/nLM9UWJT4tF60G/NyvU8G35Vv4+6pu5T2+gSOL1KXMykTA0L9g4dNqi0n70dD8xTu78P8oRVFIz0vHw+hxx+d6Yc0L7Evcx/vN3qd1aOs7L9wdkLh098k9FcWRkpWHg06Du6M6pnrLqSQGTtkOKPTVbiTdry5TRvUveFBGArbvGqA1pbHRWp2XzM+x3XEEWtQJOseZH6K6r54u2YshT52rYq+tPE6YqKyNKXiups+TVflB9DM6csmvCf5PLyywwkZ6rpnW49bR0C2ZyalPFzhU0RrQNHseNn5R+MJqPwIPTFD/35xL5rfNcE0/yW9uroz18cJV78riBxbb1xc3W83U/6U+NsXG2ofW8tyq5ziacpQvWn3BoeRDzDo8C4tiYUTtEQS5BvHmpjcB0Gq0zO8xnwpl1PhpsppoNrsZJquJRb0WEegSiLPe+ZY/l6IsPLGQHfE7eLfJuxitWk53647NlEvE7Dn2tcbt98ZsJm7MGNLm/w6AzseHCuvXodEVrNiwJCeTe+gQLi1a2FudFUUhfelSsnftxsHHBwd/P/T+/uQeiyJ96RK0bu74vfoKjpUrk75sOQmffootLc1+Tp2XF+VXrUTr7Ez82A+49MsvN74wjYagTz8hLzqGnAP7sWVmYSxXDr///Q9z9DnOPfIoODgQ+v1knC/nQ9djTUsjY/UaXNu0LrKbvih9JZZ4N2rUiLp16zJp0iT7tipVqvDAAw/w8ccfF+sc1apVo3///rz7rjr744wZMxg1ahSpqam3UpQCJBgL8d9w/lI2byw4SDlfVzJyLew6l8K55PzWiBWjWhKfnsuQH3fg5WJg+5vt0BfRGnI0Lp2+k7bgbNCx4bU2OBuKaDFXFJgzSO3651sFnt0MuWnwQ2tIPae2fF/dEtJvOlTvA+Zc2PotnFgJ6XHgXRYemAzuxZsER9y+i9kX8TB6FJjYqLRIXLr75J6Km8nINfPGgoOsPZbIm92qMKhROJtPJjF+1QnMNhseTnoeaxZJq4q+hY49uOoXHDd8yGeG4TRs2YVhse+gObaUhNrP02hbE1wMOvaMrIJx7Rj2HT/DY+lP4aXJYKFxNG7OTmiaPg+r3kPxKsteUwh1szYAsKn6BzTv97z9fZYfiueZn3fzP4fZPOuwBOXyLBcABzWVqDHoI3UpzSvavgNrP1SXW+v9A9TqD6vGYNr8JctcXHjHN7+3zkfNP6JNaBtWRa/CRefJSxuex6hzZOegHXy681NOpZ7iseqP0TSoKYtPLubtzW/j5+zH3O5zWR+zntFbRwNQ378+7zd9n1D3ULbHbWfY32q36hDXEKyKlb/7/X1XPq8tsVsYtXYUI2qPYHC1wdhyc8FqRevict1j0pb+ycWvv8ZryBC8Hn3krpTjWubERJJ/mIIlIR7FaqNM/4dwbdUKAMViIebZ58jauBGdhweeDz+MJTERS0oyZQYMIHPdOlLnzC3yvE716mGOi8USGweAxtkZ/1dfQR8WhjU5GdOJE+Ts3YfpxAmcGzbE86GHiB87FnN0NMaKFQn/eRa6Yn73KXl5mE6exHTqNPqQ4Jsm+OL2FTc23dI63nl5eezevZvXX3+9wPaOHTuypYixFUWx2WxkZGTg5eVVYHtmZibh4eFYrVZq167N2LFj7S3iQghxRUgZZ2Y90cj+s9mqznL7++7zpGTlUcZZTzlfF3xcDSRl5rHpZBJtKvnZ97fZFNIvL1WTnWclO8/K+qiLdKlRRFKs0UDvybDjB6jSU+0u6OwFD/8CUzuoSbfeBcq3U8eML3gK9v6sJuXJJ/PPkxYNP3WHIUuLl3ybMtXZ18u2Vt9PFJuvc+GHaSHEf8P83ed5ZV7+klNBnmq37GblfWhW3uemx4c2fZBTFboyIdgDg4MWcidC0ov4BtbF7/AaEjNM7EhxxqHOOAbs2oZBp8XB2ZfmGV/xcquqDGkQAWs/RJNymrrkD5eqevATTjfoSdlwddLGDScuosNKX506vnx/1VepevgLDBorW83l8XGthj1SBNeDlq+AzQrrPoI/X4bYPbB9MmN9vFjs5gqAVlGY0GISzcu14GL2RcZuHZs/l4XZG41Gw+sNCz6/d4nswpe7vyQxO5E9CXvoW7EvDQMa0mtxL3Yl7GLi/ol83OJjtsWpS3y1DW3LuvPrsCk2ErMT7S3rV9i7zxchy5zF8UvHqeVby74aBUB8Vjw5lhx+OvwTD1d+GEMxhph6dO+GR/duN93vTuj9/Ah4+60iX9M4OBDy3bdkbd6Cc4P6hbqLuzRpQu7hI+QePIhTvXp49OiORq8n4ZNPydmt9tjTh4dhCA4ma8tW4se8X+T7ZKxcScbKlfafTcePE/Pcc/g8/QyK2UzW5s3knTuHY5XKOFapgsbREa2LK4bwMLK2buPil1/aZ31HpyNs+o84VqpEwrhxaPR63Nq2xbFyZXRlyqBxuHFKaM3IwBwXh87NDZ2XF1qjscj9FLNZvUf6wjP4F8WWlYXG2fk/M1HdLSXeSUlJWK1W/P39C2z39/cnPj6+WOf44osvyMrK4qGHHrJvq1y5MjNmzKBGjRqkp6czfvx4mjVrxv79+6lQoehuoiaTCZPJZP85PT39Vi5FCPEvoddpebRxOI9eMwN695pBzNhylhfn7mNEm/I83iwSrVZDzwmbOJWYRY7Zat/3z4NxRSfeAI7u6kPP1QJqwIMzYNtEaPU/dSzf/Mcvd0O/PIbM1R/avKmuNb5klJqIT2kDDZ5QJ4Fz9QOrBfbOAv/qENpAbWE/9Dv8/Y46IZx3ebU7u5s/QgghbizQIz9pi/RxoVWFW6uI83Q2UC/8qt4yjh4QUh8t0LayH3N2xrD6aCIdqvpTzteFpuV8qBbkzl+H4qlfKQyMbkR7NiQseRMAMT4tMGbF4ZdzEuuCR+DhCSgBNfhtZwyttPvx06SSrLjxdXor6lhP8qxuMX9aGxGQoNDTtwpcPAqNnlXL0uJlOL0WoreqE64BrUPbsDh1JwBT4xPJ3rUbyrXA19mXOr5N2J6wHoDMLHeSM014X5lwLi8bEo9iCK5Lv4r9+OHAD8w+NpuOER0JdQ/l6zZf89Phn2iTGA2zB7LNRU2m2oW343zmeY5fOs7BiwdpF97OfqtmH5vNiUsneKvRW2g1WhaeXEjniM446525kHmBZ1Y+w9n0s/St0JeyHmUJcAmgY0RHepTtwcR9E0nITmDxqcU8WPHBW/rMSovWaMStbZvrvhY+aybW5GT0wfnd5Y0VKhD9hNpzIHTCBPShoSRNnETuwQNYLiah9XDHWL48TtWrow8JJfmH78nashWnevXwHf4c518YSc6u3cTserLA+2Vt2nT9crq7o/PwwBwTw4VRL+Lg7YXphNowkDp7jrqTRoOhXFmc69ZD5+4GGg3OjRvjVKs2l375hdTff8ccHX3VSbUYy5XFUL48WqMjOg93jBUrYTp1itS5c0FRKDNoEIZyZcnZvRsHX188H3wQbDaytm7FlmtCsZjJ+HslOXv2oPP1wbVpU1yaNcO5Xj1sublYL/eC1hiMGCIj0LmqFUyKopB76BB5Z8/i4O2Ng68vOh8fdJ6eaDQabDk5ZPz9NxqjEdcWLdA4O2PLyACbDY1ej0avx5adTfryFaT98Qfew57ArW3bW/rs78QtdTWPjY0lODiYLVu20KRJE/v2Dz/8kFmzZnHs2LEbHA2zZ89m2LBhLF68mPbtr78GoM1mo27durRs2ZJvvvmmyH1Gjx7NmDFjCm2X7mdCCIA90ZfoMzG/J87C55pSJ6wM9cauJDkrD2eDjo/71CA120yX6gHkWW0M+XEHAR6OfNy7JmHetzh+TVEg4RCc3Qw2M9R5FJw81dcunVOXsUk9p/5s9ICe38DBeXBsKegMMGC22jX98gOVnW8VdTZb6aZ+35Fu0Xef3FNxI9HJ2bQctxaAd7pX5YnmkXft3FfmFwn1cmLDq22wKepyZ84GXYHWuuiVkwjbrLYsK8PWAAq2Gd3RWXJQ0PC1fhjjM1oz1zCWRtpjTLV04QPLowBosWFDy+Am4bzfCIg/ALUG5I8PT42BH1pBXjaW7l/zU2ZdtqZPJjg5hjGn/uYS7jg3eBRjvYE8uSKHExd+xcFrG4G6PrzX6xnK+11umf3rVbUnV4exxNd+iA7zLy/f2OcvQt0uTxSadEJdzQPY6V+RrbV7M6DWk0zcP5H5x+dTy7cWtX1r83L9lzmfeZ4eC3tgVax0juhM+/D2vLL+FUJcQ5jTfQ59/+hLQnZCoXs6vdN06gfU5+cjP/Ppzk/xdvSmY0RHKpapSNfIroXGkSuKwoGkAxi0Bip5VSrQcn61LHMW3+//nq1xW/m166+F1k+32qxoNJrrHl+SrBkZKBZLscZqK4qC+fx59MHBaLRacvbt4+KEiVguXkSxmHGuVx9jpYrkHj5M3pmzKFYL1tRUzOcvoHV2xueZpynz6KNgtXJ2wEBMl/M0B19fXFu3JnPTJizx8erzS1EuL9V2hdbDA1t2Nlxu1b4lWq06Ge1tcggMxFi+PNbkZHKPHCn0us7TE6d69cjZvx9rkrpKisZgAAcHlOzsQvtf4daxIyHf3Nq69kUpkTHeeXl5ODs7M2/ePHr37m3fPnLkSPbt28f69euve+zcuXN57LHHmDdvHt263bx7yJNPPsn58+dZtmxZka8X1eIdGhoqwVgIAagBq/Xn6ziXnM3QphGM7lkNgORME/HpuXi7GAm43DqSZ7Hx0Pdb2ReTCoCLQceQphE0jPSiaTkftcvhZXkWG4dj06gZ4olOewtdo8w56izpWydAwsEidtDA5TF+tPofVOsNMx+AzHhwcFJbypu/CC437y55R2xWOLcZQhqCvnirSoiiSZJ498k9FTdisdpo/fk6TBYbq15qhYdT8bq7Fkd2noWRc/bRupIv/euHXn8m9ZxL5E3rhiGsnrpSBqgJ88p34PBCTIoDH1kGMUb/ExatgebZXxCPOka7Q1V/Vh5JoEqgO8tGFr1aENkpoNFwIt2BDl9twMWgY8eomqR924YgJb/36XnFhxCNmoAQ2RKGLFH/X1HgyyqQEacOlXp+N58em8myM8uY1XVWfuK96StYNTr/fV38oMXLLPT04t3tY+2b32j4BgOrDGT52eW8sfENLDbL5RHrCsNyFEa2/YLZthR+i/qNXuV68cVuddK4tqFt+brN12g0GrLN2XRb2M2+rKQO2PLwVpyNaivnpP2T8DB4sD1uO2ti1gDgafSknGc5ZnSeUeD2rDi7gqkHp3IsRU0yJ7SbUGDJNFCXY3twyYO8UOcFHqz0YKkk4CVJycsDrbZA9/G88+eJHjwErbs7oRO+s7fEK1arOhndgQPk7D+AYrFgzUgnY8Xf2DIy0IeE4DNiOK6tWuFQpgyKomBJvEju4cOYz8egmM1YEhPJPXoMjZMjZQYMAJuNlBk/YcvNxbl+fXIPHSJ7xw7QanGqXRsHPz+14qB2bdw6d8Z8/jxZmzeTtXkLuUePonVzQ1fGE41GizUz055IX6ExGHCsWQNbWhqWxItYr5oED9Tl4HBwKNhKfw1jxYp49OqJe/fu6P3vvFdhiU6uVq9ePSZOnGjfVrVqVXr16nXdydVmz57N448/zuzZs3nggQdu+h6KotCwYUNq1KjBjz/+WKxySTAWQlxrX0wqW08l81izCBz1119T9YOlR5i66Qzujg5U9Hdj17lLAISUcWL1y60wOuhQFIWohAxenLufo3HpdKkewHcD695a8g1gyYOV78L2SepDz0Mz1f8/uUpdrqz3ZKh5eShO0glY+Ez+uuJ6F2g4TF2H3JILaz5Ul0mr0gMaPQOBNQu+l6Ko/7RayE2H3x5Vu693+ez6a8xeaQkJaQCD5qnLq11t2ySIWgatX4fwprd27f8xEpfuPrmn4mYycs1YbQqezqU/wWIBigK/PAgn88fsxlQcSosDHe0///R4Q4b8uAOAA6M74mZ0ICkzD1+3wuNpl+yP5fnZe6kT5snC55qRm5uD45nVcGAOlmPLcVDM2NCiRQEUeH4PeJeDi1EwoWH+iWr2hz4/FC7v1PZwfic0GwVRf0HScQDiAqrS1SUXZwdnRtQZwYMVH8RBqyZ4m079xYtb3yPXmoufDZZEx+DsHgrP78KkAaPOyLIzy9h0YRMv1XupwBKOcZlxbDs6lzNbviJTq+HdzlOgQgcOXDzAoL/yly9z0Dpg0BrItmTj5+zH6gcLLhH29qa3WXwqf1m4rpFd+aD5BxxNPkpNXzVG5lhy6LqgK0k5SQS4BOBp9MTJwQlHnSOtQlsVWC7NbDXz+4nfaRjQkLKeZa/36d4XFIsFdLpijae25eaSd+YMxnLl1JbjO2SOi0Pj6HjTln7FZiu0/rk1LU2dJO7kKbBZcevcucB5bCYTuUeOkLNnDw6+vrh36QIODuSdOQsaNRHXODig5OWpY9BtNnSennd8TVcr8eXEJk+eTJMmTfjhhx+YMmUKhw8fJjw8nDfeeIMLFy4wc+ZMQE26Bw8ezPjx4+nTp4/9PE5OTnh4qEvHjBkzhsaNG1OhQgXS09P55ptvmDVrFps3b6Zhw4ZFluN2L1gIIa5mttqYuvEMk9efYly/mrSr4s/ifRfYdDKJjlX96Vxd7eJ9JDadrt9sLHBsJX83XuxQwb7PyiMJBHs6UTWoGN9BF3aDsw+UCVfH222frI4VvzaZVRQ1KV/7IcTuvfE5q/aCNm+Db0U4twUWPaeOUX9smZowr7ncSlF7EPT8Tk3Ij69Qx5TXGQSVuqkPZMrl8e/+1aHD+xDRAhwM6vtPaavOrAtQ/3Fo/pKsYX4dEpfuPrmn4r6WGgMTG6vLkemdiR2yjabfHbK/fPyDLjT+eDUpWXmMf7g236w+QU6elU3/a4v2mkrez1dE8d3akwxoGMrHfQpWupozk9mz8U8aNmqBZtlrcOJvMusNZ2PE83TJ/gOWvaYuiXnpHKDAsNUQUt9+/LRlW3lie2f1h5eOgbM37PsF1nwA2UkkVGiPa78ZuBivmlTsxEr4pR/7qnbhR0cYengNda/0TO38CTR+9ub35++31WU8QV3XfOBcbIqNHw/9yLd7vyXQJZAvWn1BRa+KHE85To4lh/oBarljM2P5du+3bI3dSnJuMiPrjmT8nvE46hzxdfYlISuBmV1mUs2rMlZgTtQcxu8ZT44lp1Ax5veYTyWvSgB8suMTfjn6C4EugSzqtQgnByeiM6IJdg3GQevA2bSzrI1ZS5fILgS45C+DnJSTxN7EvQS6BKLRaDh08RDeTt60C2t33cTXbDWzK2EXAS4BRHoUb5jEjSa0u54zaWdwM7jh41TCPej+Q0os8QaYOHEin332GXFxcVSvXp2vvvqKli3VbhxDhw7l7NmzrFu3DoDWrVsX2QV9yJAhzJgxA4AXX3yRBQsWEB8fj4eHB3Xq1GH06NEFxpHfjARjIcTtUBSFKRtPU8bZwIP1r59ArotKZOj0nbSr7EerSr68u/gwAAYHLRtebYOLUUe7L9ZjtSnMfbpx/li6u1dQOL5cnTU9ZgfkpkLdwVC5G+yZBYcXYu+q7lUOLp3JT5AbPgUH50NOSv75yraG4Ppqd8IribZXWUg5rXYzTz0HmZfH5Dl6qAn9/tnqbLqe4fnj1TU6tVt869fB0VN9OHMPym+1vx2WPEg5BX5Vbv8c9wCJS3ef3FNx39v9Eyx5Adq+jdLiFfpN3sruc5co6+PCmldaM29XDL/vOc/b3aoyYMo2MnItzHmqMY3LemOzKfYEfNhPu1h1NIHRPaoytNkNkrRjf8KcgSQrbjQxfcdfgdMof2kDh6q+SCVdHPqDc9RK1wG/AnDgfCpzJ43hQ/2P7FPKs6bZr7zUUU1CubAbfuwCVhO0fBXavp3/Pj/1hDOXn/cdPdUY5V1enVjU2RtrrUFo8zLRtHkTXIuY9E5R4OsakHZlTXQNjNwPnmGg0ZCUk4S7gyuGC7vAr2qBFT9sio1+S/px4tIJAJwcnNjUdxUPzO9AjFUd3+tmcOMT/GgZcwAGL4aAGqTmpnLq0klyd0wiRwMLnI24Gdx4oe4LBLsGsy5mHc+vyV8GbnDVwYyqN4pnVz1LQlYCbcPa8uvRX8m15uLk4MSwGsPoU6EPPk4+JOUk0fePvqTkXhV3gY7hHRnbbGyB8espuSksObWEWUdm2cfC1/atjbPemdahrRlQeYB9X6vNik6rI8ucxYfbPmRb3DY+bfkpDQIaFHgfk9XEL0d/4cSlE7gZ3KjlW4uukV1JNaXSdUFXTFYTfSv05amaT8lqIHdBiSbe9yIJxkKIkmS22sgyWezdF2duPcsPG07zaqdK9KodTHqumYFTtnHoQjpGBy0GBy3ujnr61QuhZUVf6oXnd4v66K+jWKwK/eqFFK91/FqKoibVV3cXTzistkYcX5GfSEe0gLNXtdKXiVQT5EXP5ifloLZsJ1xpddGo65UbXGDjFxC1HLIS8/c1usOIXZAUBRvGwZkNlw/TglavPpABDJwHFTqor3uVLX6reF4WTO8Kcfug2xfQYJi6FnriEQispba8ZCaC0Q0MtzgB3tXSzqtd5qs+UPRD4F0gcenuk3sq/hWyU9RhPJdbKjNyzSRl5hHpU3Dt6tfm7+e3XefpVTuIIE8nTiRkMnFQXfQ6DZFv/AXAr082omm5G7RcWi3wVTXIjOd18zDecvgFN00O3U0fEOjrww/pz6priA/fCTHbOfD3T/jmnCJQk8Kn5ofZ4P8If75w1Xjz/XNg4dMAJHX5AZ9G/dUK22+uWQJY66B2b5/VW61IvcxSth0Oj/6eP2FcwmFIPsmGWA0tNz2KYnBDE1Bdnb09oKY65MrZG5q9AEf+gHOb1JVFnlpfIAbuid/F4ysex4pCGwdvvkm3sCn9BAvcXGlRpiqdmr2J84+XW/HLRMJT69QJUPfMhD/U5Nr64E/oqj2AoijMPzGf8XvGk2ZKo2FAQ3bE76Cqd1V+7vIz2+O3M2rtKEyX452Xo5c9wb56TPlvUb8xaf8kzDYzlcpUYk/iHiw2Cz5OPnzW8jN7svzU30+xNW4rAB5GDzLzMrFejuNhbmEs7b0Um2Jj/N7xnE49TevQ1vx0+CfOpp8FIMgliKV9lmK2mjmSfIRz6eeYcXiG/fUr3mvyHv0q9mPZmWW8tuE1AFz0LoyoPQKDzsDW2K0MqjLI3osAICMvAzeDG8vOLOOv03/RPrw9bcLa4KZ3u2lLu9lmJjMvkzKON59I7lpppjTSTemEuoeq48oVC3rt3Zuz4W6TxFsIIf5hl7LyGDBlG8fiMwpsD/JwZMsb6rIrOXlW6n+wkqw8NajWCy/DgIZhtK/ix6aTSXSuFnD9SXuusSf6EptPJDG4SQQezpcDUk6qmmw7eUFEM5g9QB2fB2r38rqPwsXjcGCumqRX7qZO5rZ0FOz5Ceo8Ar0m5L+JzQq7flQn2cnLhK6fQ8OrljKJOwDrPs5/D1d/taXcxVftNn9ksfqA+fgK8K0EWcnqz1qtOuHc/tmQfEptHQltrCbDUX+q5zK4woA58PsT+a3vOgNY89TXOn2oLs2m0ajnyrmkXn9uKuSmqf9slvwZW7U6taXElA7zhkJ2svpA1+0LtdX+itx0tYv+HZK4dPfJPRX/JVtOJjFw6vYC2yYMrEt0SjafLlcnD9vzTge8XG4yBnfdJ+r39GXpGjfaaqaSlG1lgdcE6mZvvtz1/Kx9H0WjZXW7pSToQxjUqOBynaa/3sC4YyLZipHTPeZTPXWN2nuqXDvMTt7oD/3GUf8eLI54m1eqXMKy4UsWnrTRW7MeR405P46knIHJLSAvgyzFiIvGREJEL/wb9lPnJLmRXhPVIVKKosaNNWP5NSea8V6ejEtMomVOrhprci6pFcOhjSE6f6UTKnSCzh+rY9mv9AYrEwHDd4DOwOPLBrPz4j6qlanMzG6/sPnCZlqEtMBB60CWOYufDv/EynMr6VexHw9XepilUXNZeGIhHzd4k0DfaurwrGvsSdjDS+teJDk3hYU9F1K+THkA/jj1B78c/YUHKz5Ij3I9SM1NZW3MWow6IyFuIdT1q8vZ9LM8tOSh/LXZAX9nfyp5VWJE7RFU8a7C/ov7eeSvR+yv+zj58FClh0g3pbP/4n5mdJ6BQaeWa0fcDr7a/RWHkg8VKONHzT+iR7keAGy+sJlXN7xK86DmLDtbcLLrP3v/SZh7GBabhTXRa1gbs5bojGhQINeaS5opjaScJOr712dqp6mAmogvOrmIfYn72BW/izxbHkadER8nH4Jdg2kR0oLuZbsD6vJ0H23/CB8nH9JMaSgo1POvR/Og5tT0rUl1n+r2axm6fChmq5lOEZ0o41iGqJQoMswZWGwWqnpXtY/ZT8pJYtHJRcRmxuLj5EPDgIYFKhnuhCTeQghRCixWG8cTMnHUazkUm87P286RlGFi0YhmuDvqsVhtrIu6yMK9F1hxOB6LreBX8JcP1aJP3RBA7fI3ZeMZ6oZ50rVGICazjRyzlYr+ruSYrTT/dC0pWXkEeTjy9cN1aBjpVbhAqdHwfUs1IX56Y5EPA4D68HJht9qS4FB4Ih/S4yD5hNqKXlQtd+IxtbXbp5I6DjzxcMHX3UPAr7I6Xr1CJ+g/C+Y+Aif+LnwunUFtJb941RKVBjfIyyi8r4uv2kpuvv5yIdfl4KhOUgdQ7zFo+47ain9grvrwdYct4RKX7j65p+K/xGpTaPuFujpHOV8XXmhXgV61g7mUlcdjM3YS7OnEhEF1b34ii0md8+PQfPXnqr2IaT+ZJ37aSRuXs7wR+4J91x8s3XAPr83DndtAaH735cT0XGZuPcezrcvx1u976XP0RVrqDqLoDGh0BsjLZGX1z3nzSCjVcnexw1aFbBwZ1CiMD3vXYP3xi6z96X1G62di0xnR9vwGdk5VJ3C7yhc+7/Pys8PVVvW8LI6GPYxf7hm8900G/6rqEpvbJoB7MHT5FDaPzz+HowdKgyfRODiqla71hsCSkQXjTI9v4K9X1ArcK3yrqAl6ZjyENYXkE2yzpnPcYKCfCZxrP6J2gT+1Ro1NFTqolbh6J7UC+9QaSL+Qfz6DG3R8X40r8QfU+x/SAC5GkT2zF4c1edTpNA6H6v2AYozTtprh6BKWHZ3D+KzjhDh6USukBY/UH1WgNVlRFIYu7IUx8RjVTbkM7ToF9wqdALU7/rWzt1ttVuYdn8dPh3+ijGMZ2oa1pVN4J0Ld1R5qL659kVXRq+z7d4nswt7EvcRnxTOu5Tg6R3bGarPSdl7bQl3qr4hwj2BJb3VG/WxzNo1+bXTdy+xfqT9vN1aHL3y9W11L3qJYitx3WZ9lhLipz0pdF3QlJiOmyP3q+dezz3x/PuM8XRZ0sb/2WPXHeKneS9ctz62QxFsIIe5xiem5zN0Zw6J9Fzh1MQsvFwOvd6nMQ/VDmbcrhrcWHSLPUnjdyze6VObpVuXYciqJgVPyW0PaV/Hj+bYVqBXqSa7Zyohf96LVwNcPlMXZ0ZFdsbkEeDgSUuYOumgXR8IRmN5ZbZXu9qU6YU7yiYL7eJVTux86OKrdyR2MalJ+MQoemKg+1Exuoa6J7lUWHluuVhrkpoFbIOyYAqvfz+/aDup4c6cy6ph0J0+1W7zuqooGSw7E7lNbvGs8qLZ0b/kWNnwOKGrXSNvlIH+lm/sdkLh098k9Ff81F1JzuHAph/rhZQpNsHZLbDZY9xFs/wH6TYMKHciz2IhOyab88kFweh17Kr/CM6easOC5pgXihKIodPhqAycTM/FzM5KYYaKMNov1ETNxj1WHM11UPGhi+hYLDkR4O1M92AMfVyNPtSxLkKcTAE/O2MHAU6/QRrc//9xGNx4z/4+e5uVosfGK+RmWjmpDeV9X3l96hJlbz+Hm6MCal1urs7ubc9X1xdOuSrQcnKDxM9BsZOGVOM5tgemXk63IVjDkDzi1Fla8lV9B/Ngytbv84uEFj3X0UGNOcTl5qfHlShxxD85PyCNaqPHt6qFbtQZAmzfVcew2q1rpu3MauAVAvaHqXCy5afDbYHWZz2s1GAYdP8xf+vPIHzD/cTVuAoQ1Ua9NUdRyKbYCQxxuxmKzMP3QdFacXcGTNZ+kU0QnrDYraXlplDGWsVcWTNo3iSxzFrX9aqPT6DDqjLgb3fFx8sHP2c+e8OdZ83hx3YuU8yxH06CmlDGWIceSQ2J2ItEZ0dT0qUnDwPxJtdPz0jmbdhYfJx9MVhObLmxiZ/xOLmReYH6P+fb3P512mu1x21l1bhUWm4XKXpXxcfJBo9EQ6RFJuzC1x6HVZuXdLe/i7+xPcm4y7cLaFVpq7nZJ4i2EEPcJRVGITcvF28WAo16H1abwxoID/LbrPM3Ke5ORa+HA+TSMDlr0Oi3rXm2Nj6vaKp1psvD+ksPM231eXTlMAxMH1aNZeW8emLCZUxez6FI9gHK+rny39iRuRgfmPN0YRYGRc/bSuXoAr3SshKLA0fh0Kvi5FVi3HNS1y6/ddsX0zWfYG51KOV9XhrWIxMV4ed3Q3HS1NUCnV1vdl76kzuAeWAuWjMofh/7AJKg9sOgbc2AeHFsCHcaqx14rM1F9+HLyUifaMbrf/IHCZgVThpqYX3FiJcx/AkxpaoVAt8+hXNsbn6cYJC7dfXJPhbhDilL4ezIvCzLi1eXGruOX7ed4a6HaLdnV6MCYntXoWzeYXavncXH9Dyy0NudCQDsGNwmnb92QIodMnUnKovtXqxjCn7zk+AcO1hwONv6SHusCCPZ0onaoJ1abwiudKnL6YhZPzdptP7ZPnWC+7F9b/eHgfPj9CfIcXFnn2g2vdi9Sv8YNJuP8qac638jgxVC2lbrNZlMre3UO6ve9zaauHpKXBZW6QGhDtWL4+Ao4+Bt4hECVXmqF8en1arf8nBQ1ua3SA4LrqUOUbDa1RX7VaDUB1xnV+32ld5V/DSjX5vLs7ZcrfP2qQPYlSD9fsNwOjuq/3FS1Fb3eEHXIVtTy/CFZ/tXVJP1iFOycom6r2EVthbeaoM1bsHeWGodBrdR+8Cd19RNzjloujVatKL/eMqPipiTxFkKI+9Sx+HRe/m0/XWsE8myrcmi1GrJMFpwNOvKsNowOhYPjqYuZfLb8GCsOJ2Bw0LLm5VbEp+XSb/LWAvs5G3RsfK0NzgYH6oz9m1yzjeFtynHoQjrrj1+kVqgnUwbXw89NrUEf/cdhZm07x6zHG9K0vA82m4JNUXDQaVl+KJ5nflYfjBy0Go6O7Yxep+XQhTS+W3OSWqGePNu6iAe5fb+q64U3GAYdxtz9G3g7UqPV7oqVuxfd1f42SFy6++SeClE6TBYrb/x+EJ1Ww8sdKxHg4Wh/bdfZFHzdjIR7u9zgDKqvVx3n61Un8NOk8mXXAJq3aMehC2lczDDRsqIvuqta9T9Zdgxng46vVh1HUWDGYw1oVdFXbem8GMWTi2JZeSobN6MDi0c0o6yvKwfOp1Ij2KNg121TJmTEk+0ewQ8bTvNc6/LXrUw+eD6NcX9H0baSL0ObRZKea+a3nTE0r+BD5YCbf+eYrTZyzVbcUqPUJTgrd1Nbm1e+pyb1vb8HF291dZK1H8LpdfkHGz3USeSyktTW7ytjz73KqvOd+FbK3/fESrU7fnZywQI0ehY6fgAr34FtE4supMENAmtCzPb81nk0aoW0s7c6NK3JCKjctejjL51T54U5v1udS8a3spr4O7qrS4z6VrzpfSI1Wu29prtqwjSbDWK2qXPH5FyCWv3Va78PSOIthBD/MVabwvOz99CknA+PNlZbiH/bGcNrvx9Aq4HXu1Smf4MwPJzUQPfl31F8s+ZkgXMYHLT89nQTaod62o8FaBjhxW/PNOHzFVH8sOE0gxqHsXDvBVKzzXSs6k85P1f+17kyAEfj0ukyXu1++PuzTQvM6A7qBHMGrQ2dQ8nMUHo2KQsfNyOuV1rfS4nEpbtP7qkQ9zdFUXhjwUH+PBDHtKENip6b5BpvLjzIr9vVFtunWpblza5q6/aqIwl8ufI4R+LSKefrQt2wMvy+5zwPNwzjtU6VmLD2JBX83GhWwYe5O2P4bWcM8em5DGwUxke9a3D6YiafLDvG+Us5ZJjMBHs6seNMCl4uRhaPaEawpxPDf9nDnwfjcDM6MPupxlQP9iiyjH8fjmfCulMcjUtHURSmDmlAi/I+vPvHIQ7HpvPdwLoEX+5yX0D8QchIUIdSBdQkJseAn7sRo1ajdn9PPQehjcDoWvjYjAR1Cc9Ta9R5Tlq/CRXaq69lJqozzedlQp1H1WTcYoL5jxXdbb0ojZ5RE/uzm9XyGd3UnmVx+9XzFkWjg+p9IbIFuAaolQ5OnmpLu1ug2uNi9Wh1bL5vZej3I/hXU4cQzH8sf6JWUFdKafikunSd3lmdIDB6qzorvX81dQJX73LqEqSn16rLqrr6Q+s3wD1QXb3EnKuugOLsc/05bu4CSbyFEOI/qKhJWtZFJeLhpKdOWOElPT5ZdozJ608R6eNif5jpUNWfnDwrLT5bS1Jm/hjqhc81paK/G4OmbmdfTCoA1YPdWfBss0KtBxGvq93g6oR5suDZpgXKNPqPw2w7nczXD9cuVgvC1Ww2hZfn7ScxI5fP+tWyP8hYbQoHL6Tx3ZoTrDqaiJeLgdc6VaJGiAd6nZaK/gXXVV9+KJ4Ja0/yfNvydKwWcEtlKC6JS3ef3FMh7n9Wm0J0Snah5dOuJy3bzMi5e9l0IolP+9akb70Q+2uJGbn0+HYTCen5sWpAw1AqB7jz3h+HcdRrMVls9sUt/NyMTBxUFw8nPQ99v5VL2eZC79e5WgCfPVgTd0c9Z5OyaP35OgC8XAz89nRjyvsVjCeztp7l3T8Oc3VGFezpxLs9qvL05e7yVQPdGftAdd5fegRFUfj+0XoEejhx6EIamSYLXi4Gvl1zkiX7Y6kR7MGcpxrnD90Ccs1WjA7amy7hVcDF4+rQqpB6+dusZnWOFJ0eyrfD4hrM34djqeunI0CfpS51d2Qx7Pj+xucOa6KuiBKzAzJi1YlVz24smDhfyy0IPIILTqinM6ot62kX4PwO9efy7dXE3r4uvIeaUCcdL951G93V4WdXzdIPGnD1U8fde4So/9yD1Unvwq4/4VtxSeIthBDiphRF4WhcBpE+LjgZCnZhP5uUxc/bzpGSnceCPRfoViOQ7wbWYV3URT5ZdoyU7DzmPtWYsr6Fa+IT0nNpPW4dOWYro3tUpWO1AII8ncgyWWj52VqSs9Q1a5eNbIGjvuD7pueacXcsujV83q4YXp2vtsIHejiy6qVWuBgd+Gz5MSauO1XkMZ2rBTD50fwHj5iUbDp/vYGsPCs6rYYvH6pFr9rBt3TfikPi0t0n91SI/65cszo3yLUx49CFND7/O4oKfq60quhHs/LeWGwKnb7ewOmLWQA0KevNwEZhdKzmj9FBh9lqY9ScfZy/lE3feiFU9Hfj9MUsyvu5FmqFT881M2jKdg5eSKOivytLnm+O0UGHzaZQffQKsi8vDzqgYRiPN4vgq1XHGd6mPNWCPPhq5XHGry44uWh5P1eWPt8co4OWByZuYf/liuyrta/iz/eP1lNj1MrjfLP6BH5uRlpW9GVIkwhqhBTd8n4rFEXhzYWHmL0jGn93I8tHtiQ6JZvvN5ziRb99VDg1Q02wq/ZUW5/zMtREXu8C5dsVPSY8eps6Lj5mh7q/0V1tfU8+mT+3i9YBOn0MJ1cWnHHe4AYD50BEc/Xnk6th5buQcHnJMxdfaP262g39wl51ebicS+prXmXVSVNPrITYPfnvo3cBc9ZVXeqv0egZdXb8OySJtxBCiLsiKj6DTl9voH54GaYOqY+ns9pdy2ZTbjjL7vhVJ/hqVX4N9XcD69C9ZhDJmSa6frORhHQTz7Uux2uXu6jHpubw6vz9nLmYxaqXW5GTZ8X78iRyVpvC0gOxjF16lKRME25GB17sUJHHm0cC8NfBOF6dt5+2VfwZ0aY8m04mMXn9KRRFoWVFX758qDYA83ef55V56oy6bkYHMkwWWlfyZfrQBmg0GlKz80jLMRdrrOLNSFy6++SeCiGK60xSFquPJtC2sl+RFcS3IinTROevN5CUmVcgbpV/8y8U4IW2FXihXfkiW6R3nU1hwJRtmK0KNYI9+PKhWlS43AtrXVQiby44SGKGiVqhnvSvH8rbiw9RK8SDqYMb4OGs5+D5NHp8t6nAOdtX8ad3nWBaVPS5bkX1FXFpOQR6FO7mPm3TGcYuPQJAv3ohjOtXk/7fb2PH2RRcDDoWPNeMSgFuhY67LeYctZt49DYo2wbCm6jdzs9uUlvAU05Bw6fUCVivdmW29/hD0HyU2mp9haKoy8LpDPkTBtqsavKt2NTu7kY3db/sZHUy1rQL6kzzV/6/cjeo0e+OL08SbyGEEHfN07N2cSw+g9+fbWqfUf1mcs1Wxiw5wsYTF7mQmkOvWkF81b82Go2GFYfjeXrWbhy0GrrWCKRhpBf96oXQ9JM1pGTl0aKCD3vOXeLVTpUY2iySj/46yg8bTgMQ4e3MH883L/CwYbba0Go0BSbmudb5S9k0/3St/ed1r7RmzbFEBjQMs7f2v/TbPnxdjbzR9QYz5BaTxKW7T+6pEKK0XJlQtKyPC9Mfa0C4twsxKdm4O+rxcL5x8rsvJpWLGSbaV/ErMjm/uiJ7yf5Y1kYl2iuMQe2pFZ2Szfzd51m074K9W7tBp2XlSy2vW1l8JDadfpO3sO7V1vZJUy+k5jBnRzTfrT2JosCrnSoxvE15QO2t1uij1YDaXb5BRBni03OpHVqG4W3K4XY57m4/nUxChom/DsSRlWfhwfqhdK0eUORs9sWVnWchId3EycRMLmaYqBniQdVAd7LNVuJSc4jwcUF/+fyJGbkkZ+bh42rEx9VAdp6V/edTcXfUUy3I/da65N8FxY1NpTvzjBBCiPvC+IfrEJuaU+ykG9TugB/3qQGAxWorEJA7VQugc7UAlh+O54/9sfyxP5aMXAsDG4bx3dqTbDyRBIDh8gzuPq4GNBq14vrtblUL1fDrixHsQ8o48/mDtRiz5DBjelYjwsfF3mIOakWBVqNhVPtizMj6HzBx4kTGjRtHXFwc1apV4+uvv6ZFixZF7rtgwQImTZrEvn37MJlMVKtWjdGjR9OpUyf7PjNmzOCxxx4rdGxOTg6Ojo6FtgshxL2kc/UAetYKIio+A+3lxC7Uy/kmR6lqh3re8PWre4/1qBVE5+oF5x4J9XIm1MuZZuV9GN6mHHN2xLDmWCJlXAz2pFtRFP48GMeaY4koCvSqHcQ7iw+RnWdly8lkHqgTzJmkLNpcHrMOMLRpBM9dtfqIv7sje9/pwAMTN3MuOZsL+3IAOBybzisd82Pj/34/wNnkbPvPG08k8a6znor+bjzXuhytK6kt03ujL/HWwkNE+DhTM8STFhV88HU1kp5rYW/0JdpU9rM/VyzdH2ef0PUKnVaD1abWMiwf1cI+L8wf+2L54M+jgLpai8lis+9XPdidP4Y3t9/TQxfS2Hgiia2nk6kR7E6HqgF8tfI4w1pE0qKC7w0/l7tNEm8hhBA35ajX3VFXvaJqwT/tV5MaIR5k51nQ67T0bxCKyWJl0vpTWG0KEd7OPFhfnUTnqZblaBjpTUqWibaV/W+7HP3qhdDvqol5ruao1/H5g7WKfO2/Zu7cuYwaNYqJEyfSrFkzvv/+e7p06cKRI0cICwsrtP+GDRvo0KEDH330EZ6enkyfPp0ePXqwfft26tSpY9/P3d2dqKioAsdK0i2EuF981q8mKw7Hl/iqGTeqTC7v58bb3avydveqZOTmTw6XlmPmtfkH7GPOF+69AECYlzOtK6kJ5v6YVDQadajVR31q0L1mUKHzl3Ex8NNjDZm66TSBHk74uBrIybPa43h2noUAD0eMDjraVvHDoNPy87ZzJGflseNMCq0q+toTb71Oy5G4dI7EpfPXwXg+WVbwvb7qX4veddSYrNNqcNLriPBxwctFz77oVLIuX4ub0YGkjDy4XB+h0WjwcTWSkmWyX2+QhyOXss0cupBOfHouQZ5O6rrw8/ZzLD4DgA3HLzJhrTofTGxqDitGtbzhkLm7TbqaCyGEuKe8Om8/83afZ/IjdelcPbC0i3Pb7ue41KhRI+rWrcukSZPs26pUqcIDDzzAxx9/XKxzVKtWjf79+/Puu+8Caov3qFGjSE1Nve1y3c/3VAghStLqowlMXn+KBhFepGTl8fue8xh0WuY905SqQfnfl+eSs/Bw0tvna7kbTBYrJxIyOZ6QQbi3i30Z0Vyzlc0nkzh9MYutp5PZdjqZXLMVR72OygFuPNWy7HXjvNlqIzHDRBlnPc6Gois6TBYr5y/l4KTXEeTpRHaeha2nkqke7IG/uyPH4tN5auZu/N2NtK3sz6qjCew+d4mGkV580qfGHY/9v0LGeAshhLgv5VlsJKTnFrsL373qfo1LeXl5ODs7M2/ePHr37m3fPnLkSPbt28f69etveg6bzUZERASvvfYaI0aMANTEe9iwYQQHB2O1WqlduzZjx44t0CJ+LZPJhMmUv0xQeno6oaGh9909FUKIf1pKVh5Wm4KvW/GHiJW0K2nnPz0G+2qJGbn4uhrvahmKG+9vfwS8EEIIUQIMDtr7Pum+nyUlJWG1WvH3L9il39/fn/j4+GKd44svviArK4uHHnrIvq1y5crMmDGDP/74g9mzZ+Po6EizZs04ceLEdc/z8ccf4+HhYf8XGhp6exclhBD/MV4uhnsq6QY14S7NpBvAz82x1MogibcQQgghCrn2wURRlGI9rMyePZvRo0czd+5c/Pzyl35p3LgxjzzyCLVq1aJFixb89ttvVKxYkW+//fa653rjjTdIS0uz/4uJibn9CxJCCCFKkUyuJoQQQgg7Hx8fdDpdodbtxMTEQq3g15o7dy5PPPEE8+bNo3379jfcV6vV0qBBgxu2eBuNRozGe6vFRgghhLgd0uIthBBCCDuDwUC9evVYuXJlge0rV66kadOm1z1u9uzZDB06lF9//ZVu3brd9H0URWHfvn0EBt6/E+gJIYQQxSUt3kIIIYQo4KWXXuLRRx+lfv36NGnShB9++IHo6GieeeYZQO0CfuHCBWbOnAmoSffgwYMZP348jRs3treWOzk54eHhAcCYMWNo3LgxFSpUID09nW+++YZ9+/YxYcKE0rlIIYQQ4h8kibcQQgghCujfvz/Jycm8//77xMXFUb16df766y/Cw8MBiIuLIzo62r7/999/j8ViYfjw4QwfPty+fciQIcyYMQOA1NRUnnrqKeLj4/Hw8KBOnTps2LCBhg0b/qPXJoQQQpQGWU5MCCGEKAESl+4+uadCCCHuNbKcmBBCCCGEEEIIcQ+QxFsIIYQQQgghhChBkngLIYQQQgghhBAlSBJvIYQQQgghhBCiBEniLYQQQgghhBBClCBJvIUQQgghhBBCiBIkibcQQgghhBBCCFGCJPEWQgghhBBCCCFKkCTeQgghhBBCCCFECXIo7QLcLYqiAJCenl7KJRFCCCHy49GV+CTunMR6IYQQ95rixvt/TeKdkZEBQGhoaCmXRAghhMiXkZGBh4dHaRfjX0FivRBCiHvVzeK9RvmXVMXbbDZiY2Nxc3NDo9Hc0bnS09MJDQ0lJiYGd3f3u1TCf46Uv/Td79cg5S999/s1SPnVmu+MjAyCgoLQamVk190gsT7f/V5+uP+vQcpf+u73a5Dyl667Vf7ixvt/TYu3VqslJCTkrp7T3d39vvwlukLKX/ru92uQ8pe++/0a/uvll5buu0tifWH3e/nh/r8GKX/pu9+vQcpfuu5G+YsT76UKXgghhBBCCCGEKEGSeAshhBBCCCGEECVIEu8iGI1G3nvvPYxGY2kX5bZI+Uvf/X4NUv7Sd79fg5Rf3Ovu98/4fi8/3P/XIOUvfff7NUj5S9c/Xf5/zeRqQgghhBBCCCHEvUhavIUQQgghhBBCiBIkibcQQgghhBBCCFGCJPEWQgghhBBCCCFKkCTeQgghhBBCCCFECZLE+xoTJ04kMjISR0dH6tWrx8aNG0u7SEX6+OOPadCgAW5ubvj5+fHAAw8QFRVVYJ+hQ4ei0WgK/GvcuHEplbiw0aNHFypfQECA/XVFURg9ejRBQUE4OTnRunVrDh8+XIolLigiIqJQ+TUaDcOHDwfuvfu/YcMGevToQVBQEBqNhkWLFhV4vTj322Qy8fzzz+Pj44OLiws9e/bk/Pnz98Q1mM1m/ve//1GjRg1cXFwICgpi8ODBxMbGFjhH69atC30uDz/8cKmXH4r3O1Oan8HNyl/U34NGo2HcuHH2fUrz/hfne/N++DsQd4fE+3+GxPp/3v0e7yXW37v3HyTW3wlJvK8yd+5cRo0axVtvvcXevXtp0aIFXbp0ITo6urSLVsj69esZPnw427ZtY+XKlVgsFjp27EhWVlaB/Tp37kxcXJz9319//VVKJS5atWrVCpTv4MGD9tc+++wzvvzyS7777jt27txJQEAAHTp0ICMjoxRLnG/nzp0Fyr5y5UoAHnzwQfs+99L9z8rKolatWnz33XdFvl6c+z1q1CgWLlzInDlz2LRpE5mZmXTv3h2r1Vrq15Cdnc2ePXt455132LNnDwsWLOD48eP07Nmz0L5PPvlkgc/l+++//yeKf9PPAG7+O1Oan8HNyn91uePi4vjxxx/RaDT07du3wH6ldf+L8715P/wdiDsn8f6fJbH+n3W/x3uJ9ffu/QeJ9Xf0GSjCrmHDhsozzzxTYFvlypWV119/vZRKVHyJiYkKoKxfv96+bciQIUqvXr1Kr1A38d577ym1atUq8jWbzaYEBAQon3zyiX1bbm6u4uHhoUyePPkfKuGtGTlypFKuXDnFZrMpinJv339AWbhwof3n4tzv1NRURa/XK3PmzLHvc+HCBUWr1SrLly//x8p+xbXXUJQdO3YogHLu3Dn7tlatWikjR44s2cIVQ1Hlv9nvzL30GRTn/vfq1Utp27ZtgW33yv1XlMLfm/fj34G4PRLv/zkS60vX/R7vJdar7uX7L7G++KTF+7K8vDx2795Nx44dC2zv2LEjW7ZsKaVSFV9aWhoAXl5eBbavW7cOPz8/KlasyJNPPkliYmJpFO+6Tpw4QVBQEJGRkTz88MOcPn0agDNnzhAfH1/g8zAajbRq1eqe/Dzy8vL4+eefefzxx9FoNPbt9/r9v6I493v37t2YzeYC+wQFBVG9evV78jMB9e9Co9Hg6elZYPsvv/yCj48P1apV45VXXrlnWlbgxr8z99NnkJCQwJ9//skTTzxR6LV75f5f+735b/07EAVJvP/nSay/d/wbv+ck1pceifW3xuG2j/yXSUpKwmq14u/vX2C7v78/8fHxpVSq4lEUhZdeeonmzZtTvXp1+/YuXbrw4IMPEh4ezpkzZ3jnnXdo27Ytu3fvxmg0lmKJVY0aNWLmzJlUrFiRhIQEPvjgA5o2bcrhw4ft97yoz+PcuXOlUdwbWrRoEampqQwdOtS+7V6//1crzv2Oj4/HYDBQpkyZQvvci38jubm5vP766wwcOBB3d3f79kGDBhEZGUlAQACHDh3ijTfeYP/+/fbug6XpZr8z99Nn8NNPP+Hm5kafPn0KbL9X7n9R35v/xr8DUZjE+3+WxPp7y7/te05ifemSWH9rJPG+xtU1mKB+YNduu9eMGDGCAwcOsGnTpgLb+/fvb///6tWrU79+fcLDw/nzzz8L/YGUhi5dutj/v0aNGjRp0oRy5crx008/2SeZuF8+j2nTptGlSxeCgoLs2+71+1+U27nf9+JnYjabefjhh7HZbEycOLHAa08++aT9/6tXr06FChWoX78+e/bsoW7duv90UQu43d+Ze/Ez+PHHHxk0aBCOjo4Ftt8r9/9635vw7/k7EDd2v8SXq92P8V5i/b3p3/A9J7G+9EmsvzXS1fwyHx8fdDpdoVqMxMTEQjUi95Lnn3+eP/74g7Vr1xISEnLDfQMDAwkPD+fEiRP/UOlujYuLCzVq1ODEiRP2GU/vh8/j3LlzrFq1imHDht1wv3v5/hfnfgcEBJCXl8elS5euu8+9wGw289BDD3HmzBlWrlxZoAa8KHXr1kWv19+Tn8u1vzP3y2ewceNGoqKibvo3AaVz/6/3vflv+jsQ1yfxvnRJrC9d/5bvOYn1pU9i/a2TxPsyg8FAvXr1CnWBWLlyJU2bNi2lUl2foiiMGDGCBQsWsGbNGiIjI296THJyMjExMQQGBv4DJbx1JpOJo0ePEhgYaO+ecvXnkZeXx/r16++5z2P69On4+fnRrVu3G+53L9//4tzvevXqodfrC+wTFxfHoUOH7pnP5EogPnHiBKtWrcLb2/umxxw+fBiz2XxPfi7X/s7cD58BqK1C9erVo1atWjfd95+8/zf73vy3/B2IG5N4X7ok1peuf8P3nMT6e4PE+tsrnLhszpw5il6vV6ZNm6YcOXJEGTVqlOLi4qKcPXu2tItWyLPPPqt4eHgo69atU+Li4uz/srOzFUVRlIyMDOXll19WtmzZopw5c0ZZu3at0qRJEyU4OFhJT08v5dKrXn75ZWXdunXK6dOnlW3btindu3dX3Nzc7Pf7k08+UTw8PJQFCxYoBw8eVAYMGKAEBgbeM+VXFEWxWq1KWFiY8r///a/A9nvx/mdkZCh79+5V9u7dqwDKl19+qezdu9c+C2hx7vczzzyjhISEKKtWrVL27NmjtG3bVqlVq5ZisVhK/RrMZrPSs2dPJSQkRNm3b1+BvwuTyaQoiqKcPHlSGTNmjLJz507lzJkzyp9//qlUrlxZqVOnzj9yDTcqf3F/Z0rzM7jZ75CiKEpaWpri7OysTJo0qdDxpX3/b/a9qSj3x9+BuHMS7/85Euv/efd7vJdYf+/e/ysk1t8eSbyvMWHCBCU8PFwxGAxK3bp1CyzXcS8Bivw3ffp0RVEUJTs7W+nYsaPi6+ur6PV6JSwsTBkyZIgSHR1dugW/Sv/+/ZXAwEBFr9crQUFBSp8+fZTDhw/bX7fZbMp7772nBAQEKEajUWnZsqVy8ODBUixxYStWrFAAJSoqqsD2e/H+r127tsjfmSFDhiiKUrz7nZOTo4wYMULx8vJSnJyclO7du/+j13Sjazhz5sx1/y7Wrl2rKIqiREdHKy1btlS8vLwUg8GglCtXTnnhhReU5OTkUi9/cX9nSvMzuNnvkKIoyvfff684OTkpqamphY4v7ft/s+9NRbk//g7E3SHx/p8hsf6fd7/He4n19+79v0Ji/e3RXC6gEEIIIYQQQgghSoCM8RZCCCGEEEIIIUqQJN5CCCGEEEIIIUQJksRbCCGEEEIIIYQoQZJ4CyGEEEIIIYQQJUgSbyGEEEIIIYQQogRJ4i2EEEIIIYQQQpQgSbyFEEIIIYQQQogSJIm3EEIIIYQQQghRgiTxFkIIIYQQQgghSpAk3kIIIYQQQgghRAmSxFsIIYQQQgghhChBkngLIYQQQgghhBAlSBJvIYQQQgghhBCiBEniLYQQQgghhBBClCBJvIUQQgghhBBCiBIkibcQQgghhBBCCFGCJPEWQgghhBBCCCFKkCTeQgghhBBCCCFECZLEWwghhBBCCCGEKEGSeAshhBBCCCGEECVIEm8hhBBCCCGEEKIESeIthBBCCCGEEEKUIEm8hRBCCCGEEEKIEiSJtxBCCCGEEEIIUYIk8RZCCCGEEEIIIUqQJN5CCCGEEEIIIUQJksRbCCGEEEIIIYQoQZJ4CyGEEEIIIYQQJUgSbyGEEEIIIYQQogRJ4i2EEEIIIYQQQpQgSbyFEEIIIYQQQogSJIm3EEIIIYQQQghRgiTxFkIIIYQQQgghSpAk3kIIIYQQQgghRAmSxFsIIYQQQgghhChBkngLIYQQQgghhBAlSBJvIYQQQgghhBCiBEniLYQQQgghhBBClCBJvIUQQgghhBBCiBIkibcQQgghhBBCCFGCJPEWQgghhBBCCCFKkCTeQgghhBBCCCFECZLEW4hi2LJlC6NHjyY1NfWun3vo0KFERETc9fMKIYQQ4t60bt06NBoN69atK+2iCCH+IZJ4C1EMW7ZsYcyYMSWSeL/zzjssXLjwrp9XCCGEEEIIcW9wKO0CCPFvk5OTg5OTU7H3L1euXAmW5v5itVqxWCwYjcbSLooQQggh7oDZbEaj0eDgIOmGECAt3kLc1OjRo3n11VcBiIyMRKPR2LuHRURE0L17dxYsWECdOnVwdHRkzJgxAEyYMIGWLVvi5+eHi4sLNWrU4LPPPsNsNhc4f1FdzTUaDSNGjGDWrFlUqVIFZ2dnatWqxdKlS2+5/GPGjKFRo0Z4eXnh7u5O3bp1mTZtGoqiFNr3119/pUmTJri6uuLq6krt2rWZNm1agX2WL19Ou3bt8PDwwNnZmSpVqvDxxx/bX2/dujWtW7cudO5rr/Ps2bNoNBo+++wzPvjgAyIjIzEajaxdu5bc3FxefvllateujYeHB15eXjRp0oTFixcXOq/NZuPbb7+ldu3aODk54enpSePGjfnjjz8AeOKJJ/Dy8iI7O7vQsW3btqVatWrFvZVCCCH+oxYtWoRGo2H16tWFXps0aRIajYYDBw6wa9cuHn74YSIiInByciIiIoIBAwZw7ty5Oy7D3YyNV9ws7kdERDB06NBC57821l/pOj9r1ixefvllgoODMRqNnDx5kosXL/Lcc89RtWpVXF1d8fPzo23btmzcuLHQeU0mE++//z5VqlTB0dERb29v2rRpw5YtWwBo164dlStXLvQMoygK5cuXp1u3brdyS4X4R0kVlBA3MWzYMFJSUvj2229ZsGABgYGBAFStWhWAPXv2cPToUd5++20iIyNxcXEB4NSpUwwcOJDIyEgMBgP79+/nww8/5NixY/z44483fd8///yTnTt38v777+Pq6spnn31G7969iYqKomzZssUu/9mzZ3n66acJCwsDYNu2bTz//PNcuHCBd999177fu+++y9ixY+nTpw8vv/wyHh4eHDp0qMDDwrRp03jyySdp1aoVkydPxs/Pj+PHj3Po0KFil+da33zzDRUrVuTzzz/H3d2dChUqYDKZSElJ4ZVXXiE4OJi8vDxWrVpFnz59mD59OoMHD7YfP3ToUH7++WeeeOIJ3n//fQwGA3v27OHs2bMAjBw5kh9//JFff/2VYcOG2Y87cuQIa9euZcKECbdddiGEEP8N3bt3x8/Pj+nTp9OuXbsCr82YMYO6detSs2ZN5s+fT6VKlXj44Yfx8vIiLi6OSZMm0aBBA44cOYKPj89tl+FuxkYoXty/VW+88QZNmjRh8uTJaLVa/Pz8uHjxIgDvvfceAQEBZGZmsnDhQlq3bs3q1avtCbzFYqFLly5s3LiRUaNG0bZtWywWC9u2bSM6OpqmTZsycuRIevXqxerVq2nfvr39fZctW8apU6f45ptvbrvsQpQ4RQhxU+PGjVMA5cyZMwW2h4eHKzqdTomKirrh8VarVTGbzcrMmTMVnU6npKSk2F8bMmSIEh4eXmB/QPH391fS09Pt2+Lj4xWtVqt8/PHHt30dV8rx/vvvK97e3orNZlMURVFOnz6t6HQ6ZdCgQdc9NiMjQ3F3d1eaN29uP64orVq1Ulq1alVo+7XXeebMGQVQypUrp+Tl5d2w3BaLRTGbzcoTTzyh1KlTx759w4YNCqC89dZbNzy+VatWSu3atQtse/bZZxV3d3clIyPjhscKIYQQiqIoL730kuLk5KSkpqbatx05ckQBlG+//bbIYywWi5KZmam4uLgo48ePt29fu3atAihr16697fLcSWwsTtxXFPU5Z8iQIYW2Xxvrr1xPy5Yti13udu3aKb1797ZvnzlzpgIoU6ZMue6xVqtVKVu2rNKrV68C27t06aKUK1fuhs8nQpQ26WouxB2qWbMmFStWLLR979699OzZE29vb3Q6HXq9nsGDB2O1Wjl+/PhNz9umTRvc3NzsP/v7++Pn53fLNdFr1qyhffv2eHh42Mvx7rvvkpycTGJiIgArV67EarUyfPjw655ny5YtpKen89xzz6HRaG6pDDfSs2dP9Hp9oe3z5s2jWbNmuLq64uDggF6vZ9q0aRw9etS+z7JlywBuWG5QW7337dvH5s2bAUhPT2fWrFkMGTIEV1fXu3YtQggh/r0ef/xxcnJymDt3rn3b9OnTMRqNDBw4EIDMzEz+97//Ub58eRwcHHBwcMDV1ZWsrKwC8et23a3YWJy4fzv69u1b5PbJkydTt25dHB0d7eVevXp1oXI7Ojry+OOPX/f8Wq2WESNGsHTpUqKjowG1h+Hy5cvv+vOJEHebJN5C3KErXc+vFh0dTYsWLbhw4QLjx49n48aN7Ny5096tOScn56bn9fb2LrTNaDQW69grduzYQceOHQGYMmUKmzdvZufOnbz11lsFynGlG1hISMh1z1WcfW5HUfdvwYIFPPTQQwQHB/Pzzz+zdetWdu7cyeOPP05ubm6BMul0OgICAm74Hr169SIiIsJ+/2fMmEFWVtZdf+AQQgjx71WtWjUaNGjA9OnTAXVC0J9//plevXrh5eUFwMCBA/nuu+8YNmwYK1asYMeOHezcuRNfX99bit9FuZux8Z+M6V9++SXPPvssjRo14vfff2fbtm3s3LmTzp07F7gnFy9eJCgoCK32xunJ448/jpOTE5MnTwbUOXWcnJxumLALcS+QMd5C3KGialcXLVpEVlYWCxYsIDw83L593759/2DJYM6cOej1epYuXYqjo2OB8l3N19cXgPPnzxMaGlrkua7e50YcHR1JS0srtD0pKanI/Yu6fz///DORkZHMnTu3wOsmk6lQmaxWK/Hx8UUG+yu0Wi3Dhw/nzTff5IsvvmDixIm0a9eOSpUq3fBahBBCiKs99thjPPfccxw9epTTp08TFxfHY489BkBaWhpLly7lvffe4/XXX7cfc2Vs9p26m7GxOHEf1Jh+7flBjelFjVe/Xkxv3bo1kyZNKrA9IyOjUJk2bdqEzWa7YfLt4eHBkCFDmDp1Kq+88grTp09n4MCBeHp6XvcYIe4F0uItRDFcWd6quLXVVwLP1ctiKYrClClT7n7hblIOBwcHdDqdfVtOTg6zZs0qsF/Hjh3R6XSFguLVmjZtioeHB5MnTy5yRvQrIiIiOH78eIFAnZycbJ+RtLjlNhgMBQJ4fHx8oZlbu3TpAnDDcl8xbNgwDAYDgwYNIioqihEjRhS7PEIIIQTAgAEDcHR0ZMaMGcyYMYPg4GB7zzKNRoOiKIWWxJw6dSpWq/WO3/tuxsbixH1QY/qBAwcKbDt+/DhRUVG3VO5r78mBAwfYunVroXLn5uYyY8aMm57zhRdeICkpiX79+pGamioxXdwXpMVbiGKoUaMGAOPHj2fIkCHo9fobtpZ26NABg8HAgAEDeO2118jNzWXSpElcunTpnyoyAN26dePLL79k4MCBPPXUUyQnJ/P5558XCoARERG8+eabjB07lpycHAYMGICHhwdHjhwhKSmJMWPG4OrqyhdffMGwYcNo3749Tz75JP7+/pw8eZL9+/fz3XffAfDoo4/y/fff88gjj/Dkk0+SnJzMZ599hru7e7HLfWWJtueee45+/foRExPD2LFjCQwM5MSJE/b9WrRowaOPPsoHH3xAQkIC3bt3x2g0snfvXpydnXn++eft+3p6ejJ48GAmTZpEeHg4PXr0uMO7K4QQ4r/G09OT3r17M2PGDFJTU3nllVfsrbPu7u60bNmScePG4ePjQ0REBOvXr2fatGl3pTX2bsbG4sR9UGP6I488wnPPPUffvn05d+4cn332mb3FvLjlHjt2LO+99x6tWrUiKiqK999/n8jISCwWi32/AQMGMH36dJ555hmioqJo06YNNpuN7du3U6VKFR5++GH7vhUrVqRz584sW7aM5s2bU6tWrTu+v0KUuFKe3E2I+8Ybb7yhBAUFKVqt1j4TaXh4uNKtW7ci91+yZIlSq1YtxdHRUQkODlZeffVVZdmyZYVmMb3erObDhw8vdM7rzS56Iz/++KNSqVIlxWg0KmXLllU+/vhjZdq0aUXO0j5z5kylQYMGiqOjo+Lq6qrUqVNHmT59eoF9/vrrL6VVq1aKi4uL4uzsrFStWlX59NNPC+zz008/KVWqVFEcHR2VqlWrKnPnzr3urObjxo0rstyffPKJEhERoRiNRqVKlSrKlClTlPfee0+59mvLarUqX331lVK9enXFYDAoHh4eSpMmTZQlS5YUOue6desUQPnkk0+KfwOFEEKIq/z9998KoADK8ePHC7x2/vx5pW/fvkqZMmUUNzc3pXPnzsqhQ4cKxe/bndX8bsfGm8V9m82mfPbZZ0rZsmUVR0dHpX79+sqaNWuuO6v5vHnzCpXZZDIpr7zyihIcHKw4OjoqdevWVRYtWlTk809OTo7y7rvvKhUqVFAMBoPi7e2ttG3bVtmyZUuh886YMUMBlDlz5tzSPRSitGgU5QZ9RoUQ4l/k5ZdfZtKkScTExBQ5eZ0QQggh7g99+/Zl27ZtnD17tsjVUYS410hXcyHEv962bds4fvw4EydO5Omnn5akWwghhLgPmUwm9uzZw44dO1i4cCFffvmlJN3iviEt3kLcp64eF1UUrVZ70yU5/is0Gg3Ozs507dqV6dOny9rdQggh7hmKotx08jWdTidrVANnz54lMjISd3d3+9JtV08gK8S9TBJvIe5DVwLPjbz33nuMHj36nymQEEIIIW7LunXraNOmzQ33mT59OkOHDv1nCiSEKBGSeAtxH8rLyyu0vMe1goKCCAoK+odKJIQQQojbkZGRcdPluSIjI2WYlBD3OUm8hRBCCCGEEEKIEiQDQIUQQgghhBBCiBL0r5nV3GazERsbi5ubm0w+IYQQotQpikJGRgZBQUEy0eFdIrFeCCHEvaa48f5fk3jHxsYSGhpa2sUQQgghCoiJiSEkJKS0i/GvILFeCCHEvepm8f5fk3i7ubkB6gW7u7uXcmmEEEL816WnpxMaGmqPT+LOSawXQghxryluvP/XJN5Xupy5u7tLMBZCCHHPkC7Rd4/EeiGEEPeqm8V7GXQmhBBCCCGEEEKUIEm8hRBCCCGEEEKIEiSJtxBCCCGEEEIIUYIk8RZCCCGEEEIIIUqQJN5CCCGEEEIIIUQJksRbCCGEEEIIIYQoQZJ4CyGEEEIIIYQQJUgSbyGEEEIIIYQQogRJ4i2EEEIIIYQQQpQgSbyFEEIIIYQQQogSJIm3EEIIIYQQQghRgiTxFkIIIYQQQgghSpAk3kIIIYQQQgghRAmSxFsIIYQQQgghhChBkngLIYQQQgghhBAlSBJvIYQQpcJstbHicDwXUnOKtf9vu2L4cuVxbDalhEsmhBBCFGazKWw5lUSu2VraRSkkLcfMqiMJfLD0CG8uPEh8Wm5pF0lcw6G0CyCEEOK/6fO/o/h+/Wk0Gmhe3oePetcg1MsZgH0xqYxbcYz/da5MzRBPpm8+w5glRwBoGOFF8wo+d708eRYbi/ddoF+9EDQazV0/vxBCiPvb9xtO8+nyYwxtGsHontWuu19OnpXUnDwCPZxu+70URSHPasPooLvhfgv2nOfHzWc4HJuOcrleWquB/3WuXGjf5EwTL8zZS06elZ61ghjaLLLQPlabwsYTF2ldyc++7XhCBmFezjjq1bLEpeVwLC6DFhV8cNCp7bgnEjL4dHkUUQnpAFQNdOfVTpUp7+daZLnzLDaOxaeTkG4ix2yleXkfvFwMN78x9zFJvIUQQpSo5EwT36w+QZvKfgUCea0QTwAUBTaeSOL52Xv5/dmm6LQaPl8RxeaTyfT8bjOhXk7EpKit4h2q+tOsvPddL2OexcazP+9m9bFELDaFAQ3D7vp7CCGEuH+ZLFY+XX4MgBlbzvJWtyrodYU7DyuKwpDpO9hz7hJzn25CvfAyt/Q+JxMzeGrWbmJSsjFbFdpW9uPtblUo61swgbXaFN5fcpiftp6zbyvr40Kjsl5EeLvg4aQvsH+u2cqTM3exJzoVgAYRXkW+/9ilR5ix5SwvtKvASx0qkpZt5tFp23F31DPuwVo46XUMmLKNlKw8XupQkRfaVcBksTJgyjaSMvPs54lJyWH10USCPJ14plU5BjYKg0tnOWVy438Lozgcm06O2Uob7V7+5zCHtxiAR60evNejGk6GgpUNuWYrf+yPZfPJJI4nZGIyW6kc6MaEgXULVJTHpuaw82wKaTlmLFaFQxfSOBybTpNy3rzXoyomi40HJ2+la41Anm5ZFq32n61kl8RbCCFEiXr2lz3sOJPC/N3nWfdqG3zdjAB0rRHI6Y+6cjopi94TNrMvJpWZW8/yWLNIPu5Tgy/+juLPg3H2pPuxZhG8272qPcjGp+Xy645o5u+KITHDRMNIL359sjGgPpD8eTAOZ72OmiEe+Lk7Amr39kV7L7DkQBzfDqiDo16L0UGHVgMHLqTh5WKgjLO+iKsQQghxP1l7LJH5e84zpmc1fFyNd3y+5YfiC/y87XQyqdlmvlx5nE/71qRhpJrIajQayvm6suNMCh/+eYTfn216S72o9sWkcT4lB7NVbb5ecyyRjScu0q6yPz1qBdGtZiCgxrkVhxMAeL5teR5pHI7/5Vh3haIozNkZw5ydMTjpteyJTsXDSc9bXatQNci9wL4J6bl8/NdRFu2LBaBygBsAZ5OzcLRm43jxHH0mZuBi1JORawGwV1IbHXS80K4Cm04k8UTzSGwKTN14mtXHEolOySYqLg3Wj4O1HxDoU4OD51/GhIH2TseZqHyNATPvKFNps7MqgxqFUyPEA4CP/jrKqqMJJGfmkZZjLlBenVZjv6+5ZivNP11TIPG/Wtsqfmg0GowOWlpW9OHT5cfYfiaZKYPrF1l5UlIk8RZCiH+ZQxfSyDFbr1ubfSNWm4Lucg2wcrnPmkaj4WRiJtvPJOPhpMfDSY9Oo6Fp+cLdvQ9dSCM9x0y9iDL27nFjelajy/iNZOVZGb/6OG90qYJOq8FRr0Or1VDez5XXu1bmrYWHGLPkCLVDPakTVoavH67DB71rsOmEOp6uZ60ge5Bdf/wiw3/ZQ6bJYn9vm5I/9vvDP4/y4+Yz9p89nfV0rOrPBw/UYNyKKBIzTAz+cQfnU7J5pVMl2lX2Y0CDUAY3jbgrD2hCCCFK16T1p9hxJgVHBx1fPFTrjs/XvWYQRgcdr87fT0auhV+3R7PscjL+xIyd7HqnvT3uvdCuPL/vOc+e6FSWHIhj++lksvOsfPlQLSw2hVfn7aeMi4G3ulZBq9GwaN8FvF2NtCjvQ796IdQO9SQtx4yr0YGPlx1lXdRFlh+OZ/uZZJqV98bT2YDZakOn1TBhYF17Mn6traeTeWPBQfvPep2GyY/Uo0m5/J5jcWk5fLfmJAv3XiA7z4pGA292qULXGoGQeZFaZ2awVv8dWmMqsyzteSf3MWoEezLz8YaUuapr+KONwxncJML+c5Ny3hw/sA1t7F5CMxbD2rkAOCcdZEWV5RgqtCZw3Rdo8tSEOkiTwlfhW9Fq26onsJjIjj9O8sVc0nEm2NOF/rV9qONtxdHgQKYxvwed0UFLRq4FnVZD9WAPQjzVLv4RPs4EeDjRu06wfd+yTtk4O9gI93L+R5NuAI2iKP+KWWrS09Px8PAgLS0Nd3f3mx8ghBD3qaRMEzvOpNC5WkChblLnkrPo+NUGLDaFlS+2LNQ1LddsZd6uGOqElaF6sAeJ6bl8teoEjct6UcbZwCfLjvFl/1pUDnDn0IU0Xv5tP2V9XVhxOJ6r5zQzOGg5/kGXAuc+EptOz+82YbEp1Asvw7ynm9jLt/VUMgOmbEOn1eDvZuSBOsG8dtX4M5tN4eEftrHjbArPtS5X4LWrxaXlMPTHnUQlZABQM8SDJ1uUpX5EGXQaDX7ujvy87RxvLzoEQHk/V05fzLSXfUSb8uh1Wr5addx+zs8frEW/eiG38AkUj8Slu0/uqRCiuNZFJTJ0+k6MDlr2vNMBF2N+e2NOnpXHZuwgO8/K3KeaFOrafCObTyYxaOp2AFpW9GXD8YsAvNCuAuFezjxQJxidVsPYpUeYtim/AviFtuV5qWMlAEb8uoelB+J4qH4IuWYbf+xXW5lHtqvAix0qFnzD5FPE7VzEgrzG7Ekx8Hy7CtQO9Sy4j6LAdVrVZ2w+w/HETLxdDLSr4q8ea7XA3lkcSHfkgb9dsV2eb7tOqAefNVOokLkbTq2Gs5tAsRU43z6/3pTt9wHu5gRY8RakXYCe30C5NgXf+OB8+H0YcNXDQ93BsGdmwf3Cm0PNB2HJSDC6q/vEH4CYHWC5wQRxBldwC1Sv268KZ2q9jJ+7Iy57foCk45BzCYLrQfOXwNUPorfBxi/g7EZsehcIa4y2Zn+o1f/671FMxY1N0uIthBD3EatNYej0HRy6kM473avyRPP8iVEUReHtRYcwWdQgmWsuGCzPX8rm2Z/3EJ+ey4SBdbHaFJ6fvZftZ1KYvSMavU6D2aowZ0cMo3tWY/7u80QlZNiT3IaRXqBAeq65UC2xzabwzuJDWGwKep2G3ecucSw+w96VrUk5b9pX8WPV0URi03JZdiieke0r2FsHtFoNUwbXZ/2Ji3StHnDd6zc66DiTlAVAnzrBfNK3JgaH/LIoioLRQYvRQcvzbcszom0FsvMsHIlN50xSFh2q+pNntfHd2hOYrQplnPV0v9JSYDWDTrqZCyHEvWDD8YvUDvPE3bH438uKoth7RrWs4EtZHxdOJ2WxYM95Hr3cGqsoCq/9foBtp1MA+GX7OYa1KAvAwfNp/H0knn71QkjPsRDp64LFaiPHbLVPlNYo0osX21ekQ1V/qgS6MXdnDK8vOMg3q08A4KDT0Kt2MM+1LsecHdFk5VnxcTXSuXp+q3STct4sPRDHb7vOq8doNbgYHehZOyj/YjLiYfX7sH8OgYqV4b6V4fEV4OSZv09uOix+DmJ2wsO/QEj9Qvek0ARqiqImuft+piawyejDIpcHqdjpadqdGYdm0S8F9w+qA01GqEnw4hHUTlwIExcW3GdWb6jYGTJiQe8CYY1gy7eAAiENwLs8VO8LFTqAsw9s+hIcHKHZKGg+CnRG2DEVEg7C1u/yz6szgtVU8L20evW8eZmQrN5zko4TeewvUKwFKwriD8KeWer+V23XmrPUigX/qsCdJ97FJYm3EELco6w2Ba2GAmPD5u6M4dAFdcbQSetOMqBhKM4G9at86YE4Np5IQqOBNS+3JtLHBUVRWHogjj/2x7LpRBI5ZivVgtwp5+vCxLUn2X4mBSe9DquikGexUdbHxT4T6osdKlLR342TiZn0qh1ErWtr2C9bfiiOSetPsz8mFWeDjlUvtSLLZCnUZfvd7tXIyLVQO8yTke0qFJqp1ePSQXomLYXc58Cl6FnLvVwM/DC4HkmZefStG1xo3FyeVW05GNosguFtygPgbHCgfoQX9a/qet+9ZhAL915gYKMwHG3Z8OuTcGY99BgPNR+62UcjhBCiBM3ZEc0bCw/SvLwPPw5tcNMuwTEp2fbxwI56HR88UJ1etYMZ3CSc0UvUycIGNQpHq9WQY7aSlJGfzE1ef4pBjcJxwMIrv+0jKjGTb9ecBMDd0YEwb2eik7MZ/3Ad2lT2w0GnZWT7Cvbj+zcIZfG+WLaeTsbf3ajGpUtn8Y7Zyex+5TmY7ky/CBNGp0uAWhk9qFE4eq2W134/QC39eWb5/YyLbxg6iz9kBcPx5Wprcm6q+iZ6Z7h4DH4bDHUegdw0NZHcNR0uHlX3mfsIPLUe3Pzzb0zSSYj6S42pNfsDGlj5Duz7GTRacPQgKCeJ57InwZ+z1GRWo1WT6IgWUKkLeF2VuBtcYcPnkHAIUKDWALXCes9MOL4sf7/oLep/q/eDPlNAe9Xn1/YdCGsCflXAMzR/+wMTYcM4cA8C38oQ3hR8KoI1D0xqAwA6vdoqbjVDyinITlET822T4MTf6j4VO0O1PqB3VO/P6bXqdqcyavLfbKR6/85ugtCGN/y9utukq7kQQtxDTl/M5Nft0Sw9EEdCRi573+mAp7M6hmry+lN8skydUdWgUycI+eCBGvi6GZm++QzjVkRhsth4sX1FRravgMVqY/SSw/y8Ldp+/lohHkwYVJeQMs78tjOG9/44zAcPVKdOmCcL9qhLaUX4uBS7vFabQrsv1nE2ORuAN7tW5qnGgbDgSSgTAR0/UAP52o/UQFrnUbBZIGa7WtvtFgCu/nByJcx/XK1Rr9QVBsyG0+vVgNlkROFEPCMBdk4BmxXqPwbuIZAWDU5e4HjzGJBpsrBh/3E6lonHYdW7ai07ABro8TXUG1rse3A9EpfuPrmnQpQui9XGj+sOU9bfm/bVg25+wG06eD6Nh77fSo7ZSr96IXzSpwY2BbafSaZFBV/7fgnpuUzdeJqZW8/Ze3uB2vt4bK/q9KodRJOP15BpstCmki/v9qhGpI8LFouF+IVvsuPYOV7JeoRRjcvwwumnyTWZmGTpwXRTazR6JzxyzzNRP57DSiTVh35DtXKXV7zISoKTq6FyNzC6kmmysPNsCk3KeuNIHnzXUI1JV9MZYdhKCMwfb35201zC1o9Ca84u+kYE1oZuX4DWAaZ3gaL2cw0Ao5va+hvaGIYsUW/Ab4PVpPuKkAZqwhq3T/251wQ1Ed37M6z9UO2abXSHftOhQvsbf0DZKWpC7Ha5h9qJVWoc9a4A6Rfg2FLwCIPuX4HDP7RE2LmtYHCBwJoFt186Cw5OanfzEloqtLixSRJvIYQoQZkmCzl5VvtM3tdjttr44u/jfL/hFFd/K696qRXl/Vw5Fp9O5683AlDBz5Xfnm5in9Rk1tazvLP4MKCON/vh0Xo46nUs3neBkXP2odHAUy3K0qNWEFUD3QuMC49Py82vob9NR2LT+XJlFI56HV/1r41+8xew5gP1xYdmqUn1lTFdgbXV7nOZ8dc9HwBNX1BrsG1mdQxX6zcg8SikRqtdyU6vB4s62zkanVoLb0oDnQHKdwDnMpB5Ecq3gwbDQKtTf3b0UFsQVr4L++dgH3vm4gdlW8HBeerPPb+Duo/e9j0BiUslQe6pEKVr6rKtPLStN7H4oH/0d8qVr1Rwh+jtsP4TaPEKRDS77nkUReHNhQepHuzBoEbh9u17oy/h4aSnrK8rK48k8NSsXSgKNC3nTUpWHk4GHQufawaZiZCdzNNT11Ireyt1NCdZ4fc4Xbr1ISohg9MXs3ihXQW8XAz8uj2a9/44hNmqMOuJhmrivmqM2t0ZGGN+lEqaGB52WJdfvpAG2IYuI376owRfuNyS6xYIDZ9SW07XjIXsZKjYRa0ovjqGbhinxkC9i5qc2sxq92ibGXwqwVPrIHYvbPgMTl9+z8iWaiX0wfnqz2XC1bHOTV/IHwJ1cpVaiW1wUcug0YKzN7R4Gcw58EMbNQ7Wf0KtrF7/qZqwRzSHC3vApPaWw+gOnT5Uz39Fdgoc+h3KtQXvcsX8bRBXSOIthBCl7EhsOo/N2EFCuolaIR50rBZAx6r+5Jpt9qUysvMsLNobyy/bz3E4Vg2KbSv7MahRGDVDPPFyMaDTajBbbXy96jh/H07g0341qRuWvy7orrMpPDZ9J693rczAhmFoNBpyzVaafbKGHJOJRbV2UrF8RagzSD1AUSDpBERvVYP91d3IrsdqgZwUtcb4Wmc2wqrR6oNH509hZi/Iu9wtzNFD7dIF6kOIWR2fjbO3+nNGnPowAlB3iLr/lm/yz+3glJ9gXyukgfoAcuXBRaNTk/Jr+ddQu6IlHVcfVLT6/DFjZSIgtBG0eQs8w9SE/NACeHyZ+vMdkLh098k9Ff81cWk5xKXlFvjOv1usNoXkTJO63OINJue64tTFTKaNf4+PHKYCkKj1JcOnNmWz9qEJrq+2ni4dBXmZKOU7Ett9JsGXZ5dGUSD5lNr918WPH7dd4P2lR9BpNawY1ZJAD0cMDlp6fLuJqIQMvh1Qh+41g1hxKI5Rc/eRc3nOklohHiyoewDdyrcKTfqluAejGb4djq+AndOg8TNQuTvsnUVq9CF+c32EpzrUVlt4Fw+3H2fSOKJXTGhR1CR2x1Q1ga07OH98sGc4pJ6jSL2/V7tlx+4BR0/4tb8a6/pOU3tw5WWq8WlSU7XS2dlbTdpBTYwbPQPtR6sJds4ltQLZUPyeZ3bHV6jvjQJo1P/2nQY1+kHaebWywdEDWr0Grn4kZifiafTEoCvZFumknCRWnF1BNe9q1ParfcfnW3p6KWfTzvJUzadKvOzFJYm3EEKUIJPFyuHYdGoGe+BweezZjjMp1AnzRK/TcjIxk94TNpNx1XJXVzjqtWx4rQ1+bo5kmSzUGbuSPIsNDyc9n/atUWACluKwWG1YbAqOB39Va9HLtYVybbiQpcFz9au4HPpZ3XHgPPAqq44DuzImzCMUntmk1oAnHlHHXGl1YMmDC7vVpDztPCx6DpKioOWr0Op1OLFCrX2PPwjndxYuVEBN9WEj5bT6c6NnoOnzsP17CKgBVR9Qu5/ZbOqDhjUP3AMhLxsmNlJbtiNbwoM/qV3gYnaos5MG1FDLVyZSfV2jUSsRzDlq2ZOOw7E/AY362uZv1AeoawXWgm5fQUi9gtsVRS2P860vxXYtiUt3n9xT8V+SZ7HR9ot1tKzoy0e9a9zVc8en5fLUrF0cOJ/G0nKLqH5pNQz8Tf2e3f49pJ+H9mPU79vLFEUh5of+hMWtwIIWB2zXPX+2xokaOT/wRMsKvNmxLMwZpPZ+AiwOzrybM4BfrW15u1tV4tJymb0jmiZlvVl9LBFXowOb/tcGz/Tj8Es/LOY8ttsqk+JZnXZlnXHe9pX6Jk5eKFo9hDVCE7dPjRvhzdXxxVeScrdAtYIXoObDajI+raMac1q8oo7zjdmmvl69H/Sbpibbf4zIv5gqPdXket8vakVv8kmo2gvQqK37Dk7q+a6u+A1pAE+sLFiZcXIV/NxX/X8HJ6j1MDR/UW3dvlsur5UNQI2HoO+UInc7lHSIR5c9SgXPCvzc9efrJrC74nex+NRinq75NCFuIaw6t4rV0avpW6Ev9QPUidwsNgspuSn4OResmDdZTXy47UOWnF6CxWbBoDUwucNkGgQ0uO3L+/vs37y8/mUAukZ25dOWnzJx30QOJR1iZN2ReBo9WX52OS56F9qGtcXL8c5jeXFI4i2EEHfBzrMpfPzXUaoFeTD2geoARCdn88zPuzkSl045XxcebRzO7uhUluyP5fFmkbzbo+rlGcP3kJSZxyd9arD9TAorDsez+WQSrkYHJgysa18H++NlR3F31PNgvRC15eFmYveq3flq9Ve7m8Hlmu6rJgXTGcG/mloDf4Wrv1q7nn5BrVHXO6mt0ZW7q+PGTq1Rxzb3GA9LRsHu6UW/v6Nn/oQvoLYi1xuqJupx+9VtQ5aoSeysB9Qk+ZmNxa/BTzmtXk+dR9Rxa3ciIx4OzAX3YHU2VXOO2qXOt1KBB8qSIHHp7pN7Kv5LFu29wKi5+2hTyZepQxqg0964Rfq6clJh51So1IVMz0qsOpLAx8uOkpBuoqImhuWG19FqFDVJrdkfNn+tHjdovvq9eYXNBuPKQU4K2xp+y7ktv5Or96B913447p2Gd+w6zrg3wCvtMB6abHqaxvJon548eHY0HF6IotGhoEGrqBXSBz3bUX3QJ7y9ycT87epkZiYM6hKPwakws2d+y/C1WrwCbd/OT2xPrVFn1r4iuL4aKxWr2sprylCTcacyauVqpW7qLOAXo+D7FmpsHL4DPEOxWM0oM3uhP7dZPdczmyGgeuEyWC0wtV3+mGnfKmqLttUCQ5eos4Ff68hitZt89b53pYK3EJsNVr0Lyaeh9yRw9OB02mnMVjOVvPKHBTyz8hk2x26mnn89ZnSewYGLB5hycAruBncqeFagll8t4rPieWvTW7QMacm4luNYeW4lr298HeXyEK2yHmUxWU0kZifi5+zH8r7L7ee32Cy8tO4l1saoE5t5O3qTlpfGJy0+oVNEJ6w2K9vjt2OxWYhOjyYhO4FGgY0Icwtj8anFjKg9wj4E7vil4+g0OuKy4nhp3UvkXO4F932H72ka1JSY9Bj6/NGHPFseGjRYL1eA6DQ6qnpX5Zeuv9jPZbKaMOqMpOepPQzdDXcnjshyYkIIcRvyLDZmbFHX3VQU+PzvKMxWhQCP/IR45tazHIlTv7RPXcxi9JIj9tfMVhs2m4JOq+HLh2oD4KjXUdbXlQENw8g1W9HrtAUeoN7oUuXGhUo5DYcXqgVKOAyHF6jbN4yDNm+qy3QsfFrdFtYE0mPVLnFXku6OH8LeWeqMqKCOMRu6FFJj4MeO6iQoV+z+SW1J3vOT+rNGqz6s1HgIwhrD8jfUpNvRA2oNhOC6as2+VyTkZcH6z9SxZZEt1eOf2wYuvrfWbc6rLDR+tvj734hbgDqD6RWOHvmTwYg7NnHiRMaNG0dcXBzVqlXj66+/pkWLFtfdf8KECXz33XecPXuWsLAw3nrrLQYPHnzd/YX4r1IUhamb1B5D9cLL2Iccvb/kCF2qB1A/wqvAUoo3tHoM7PoR27pPmGZ5gG/yemJFRwU/V77S/Y320uU2uIy4/KQbsB34DW2FDmQlnEKn1eFoTlWHHBncaNxpAE41ehLq5YyTXkfTZY445/YmNtebH/Rf0UG3mx9a5BCQNRcOL8Sm0fNY3itssFbjKd2fvKafS43U1TChAe+5BDHWGEcybnwbOZm+5RT4oZeadAfVgQ7vqz2r4g/ltza3eLlga3K5tmpM2v8rVOkB/WZw7ORfrD7+O4+1/ADn3TPVMdU5l9RJvx6YcHn958rqTOBaHXiGcjH7In3+6INJn0PtkDCG+TSgYVFJN4DOAQbMgYPzyI1syRGdjWreVTFqDQVn8b7qM/0i6wQn009S9vAlqnhVoUFAAwJcAgrtdyzlGOHu4TjrnYv3GV+Wa8vjHaOJc64WXk09jsVm4fWNr2NTbEzpOIVDSYf4Leo3jqaovd7GNhsLgLODM+ti1hV5TneDO1qNlnKe5fAwehDmHsaR5COcTjtt3yclN8We1AI0m92MbEs2Bq2B8W3H0yCgAUeTj9q7mpttZp5e+XSB95lxeAYaNCgo+Dv781AltTHh4+0fsythl32/RoGN+KLVF3gY1SF7TnonmgU3Y3X0agDq+NUh15LL0ZSj5FhyCsxh8+TfTxKXFUdCVgKvNniVR6ve2Vwut0oSbyHEf9rVS3ZZrDZGztnLskMFJ/6qF16Gx69aB/PVzpXIMVsZ0jSCRXsvsONMCnXDy9C1RiC1r1pyy1F/TYuqJQ/H9R+pY5ubv6gGbQBTptoa4VNBnajlSsA256rd2LZOULux2Wkud5+LhT9fyt8cVAcGL1ZbsxOPqktruAWqLeMRzWFGdzXpHLxYHavt6qcu67HqPTXZdQ+Gsxth/hOAoj689P5eLd+V5UnCmqitCFV7Fm6NNrhAhzEFt/leM/GO+NeYO3cuo0aNYuLEiTRr1ozvv/+eLl26cOTIEcLCCo+PnzRpEm+88QZTpkyhQYMG7NixgyeffJIyZcrQo0ePUrgCIUqfoiikZpvtk2Vesf1MCocupOOo1zLw8uRjE9eeYta2c8zapo417lDVnymDC6/bXIAlT624BbQ2MyO18wh0yuB8k/d5ukoeLtPWqPs9NBMWjwBTOiut9eig203eoT/Iq/ccjjM7Y7ZBbEQXgkCNJzp9gSUmh7Uoy7gVZtwcHQip2QEO7CYgbo06hAk41fgD1q8NpXKAG2XqvIot4nF0m7+E48sxZMWCBnxJ593ccWh+N0J2kjq06NFF6rrVVypzb6TXd5jrDSXO3Y8wnQMfnZ7P3pS9pB/7mTdavQbnNkPsPnhwhr23mKIoaPyr2k/x5+k/STWlArDfyZnoql1piDpO+afDP9EurF3BccrugeQ2eophfw9j/8X9ODs40z68PS/WexEfp4KrcSw5vYSfjqiV2psvqK3pnSI68XmrzwGw2qxczLnI3Ki5TD04lVC3UL5r9x1lPcraz3E+4zzzjs/jYNJBnqzxJJEekXy791veavQWznpn3tn8DsvPqi3Pj6943J7IVvGqgpvBjW/3fktKrrp+ee/yvQl1U5fzKl+mPG83eptLpktEpUSxO2E3l0yX6F+pP280fAOdVkclr0r81v03/F38ScxO5FjKMTyMHvg5+xHoEohWoz677EvcR7YlG61Gy7hW42ge3BygwH3TaDRULFMRrUZLsGsw7gZ3Vp1bRYY5g9q+tWkZon7eZpsZq2LF2cEZT6MnVbyr8F6T9+xJN4CPkw9ft/ma06mn0Wg0RHqoz2txmXGkmFLs+5msJg4nHSbPpj5PnUk7c5NfqLtPupoLIf5TYlKyGTlnLxabwm9PN2HV0QQ+WXaMRxqH07isNw9O3oIGDW0q+xKflkvHagE826pcgZnAb4vFBL/9n72zDrOieuP4Z25udzdLd3dId4ugSEiICKL8pEXBFlQQEGlQQJQQQUBSumPpXHaX7c67eXN+f8xyl3UBQRFrPs/D470zZ86cGeTOvOe87/c7tMTnsmJH6DZXWpneNq6kFtq9omQdEvpM6XTvkJaSEJhSA/WHSiltp5dIaWs58VLQO2jzw4XSCrNA41gS8N8l+Qq4lZdWOxY2lFLzBCWMPS1NBsj8Lv7tz6XGjRtTr149Fi9ebN1WtWpVevfuzSeffFKmfbNmzWjevDmfffaZddv48eM5d+4cx44de6Rz/tvvqcx/j+VHovho5w3mP1+HXnX80ZvMbL2QwOJDkURnFDCwcZC1vju/sIiY+Z3QFeh50fAWZpScmtauVEYWQNi1m9SJ+Rpl9d5SOdH3A8DBG7HNdITtr0uNOn4El9dLv/9Ve8KAtZAegZh+i92GOtTY3JpAIZV00QkPQVd60J1nS7XS92C2iOy+mkydIBf8C8Nh6T2BsmdVCkceJafIXGasZMdJtdlaB1jds6SMSesErxx5NPFPIN+Yz52cOwzfMxwbpQ0/9/2ZlutbYhbNKAUlP/b8kVDHQMmyUutIWkEaEw9PpNBUyOL2i3G3dQdgwI4BXM+4zsiaI+ke2p3yLuXJ0efw0u6XiMiOQKvUMrHBRH6J+YXZrWbjauPKlCNTrMHuXXzsffj8mc+p7SlZh2UVZdFza0+y9dn0LN8TR40jl1Iv0bdSX56r9BwFxgKmHJnCuZRz5BnzrP04qh1Z120d5ZzLsTNqZ6lUbwEBVxtXMosy6Vm+Jx+1+IhrGdd448Ab1PWqax3TsxWfZWqjqdiobFh7fS2fnv0UpaBke5/t1sD714iiSGZRpvW+PA5mi5lD8Ydw1bpSz7veIx9XYCzgZuZNanrWRK1QP/Z5H4UcfQ7hWeEEOwWXqUn/I8ip5jIyMv9pcgqMLDhwm47VvGlUzg1BEDgVlcGr34aRVWAk0M2WrAID+66nEJ9VSFa+gTqBLiwf0gCjWaRDNe8nOJgE+GmMJMqiKn7puL0X5t2TvuboK9VZZ9yG9QOh86ySoPvZlVI92K8VZ5u/Lv15VO7Wg/8an2LRHvfy0GC45I9d/yU56JZ5IAaDgbCwMKZOnVpqe8eOHTlx4sR9j9Hr9djYlH7ptrW15cyZMxiNRtTqsi9aer0evV5v/a7T6cq0kZH5R2GxSGU/HhVBqWb/zRRAKmvqVtOXqZuvsOVCAgDOtmpGtSxZ7bSPPUS1oouggP7u0XyfUZ6LcVl0di4R5Fx18Cr1Dw5GqYiSxMDuehrXeBah/lDJ0/jYXNg7Xdpu6wrtZkifPSogeFSgC5AU8Rxc/goPQUcRGoxaVxz10lgJbV3msowWPd1qFY/DqUZpR4uWb2KrVWOrvU8w5RIo/QHoswS+f1763GshuJUjLCUMR40jlVwrPfS2bg7fzJywOVhEC4WmQjaFb7LW+ppFM5+d+4zF7ReDUk2sLpZR+0aRkCfd50lHJrG0w1KS8pK4nnEdpaBkcLXBVmGuKUemEJEdgYAgCYad/giAOefm8F7z91ApVKgUKpa2X4pKoWLGiRnE6GJYe30ttZ+RAu/d0bvJ1mdTybUS7zZ7t0xgabQYidZFW4Pul2u+TFhKGHdy7uDnIPmlN/dvjovWhSpuVXCzdePnqJ/JLMokwCGAcXXHAVDdvTo7++5Eo9QwqNogCk2FNPFtYj3PgMoDiNXFUsmt0gODboCC02fImT0bm8mTsG/a9KH3/tcoFUraBbV7rGMA7NR2jxWo38WUmQmCgMr1t5X/nbXOf0jc7Y8iB94yMjL/Sg7eSmXlsTscvZ3GptHN2BwWz6zdNzGYLFTyduDrYY3wcNBy8GYqgDXQbl35d8yA3rVJSbkCRTpp5r5KD0ms5dxKycLDkCupmA5cL3lO/zCseJbfCSp1hq6fSu2/f0FK976rqFp7oGQF8rTo/AlU6gTlnnl655T5x5Geno7ZbMbbu/QElbe3N8nJ9/do79SpEytWrKB3797Uq1ePsLAwVq1ahdFoJD09HV/fsmr+n3zyCe+99959epOR+fthNFtQKYRSNaWliA+DXZMh4Rz41ETstYibyZL1YlxmIdsuJTKzRzXUSoFa9ln0CsjH0f2eGt97BC/7aM/yPeW5EJtNNV8p7TbI3kj3iJl4KYozqAy5Uno1kBLcA2+QrBNjT0nK3wGN4LmvwTmgzFB9WwyBy18BoG03DZsK7eDrbpLN4q9KiOJ0cYzcO5Jx9cbRPbS7VC8d3Bxu7cTiGoyiet9Hu4GVu0iCbmYjVOnKz1E/M/WoNLnXq3wvxtcfXyZ9+y4H4g5gucdebEfUDrRKLXW96nIu5RwFxgIMZgOZRZkM2zOM1IJUAhwCyCzK5HzKeS6mXuRC6gVAqiG+Vw17XN1xJOQlMKvVLBZfXMzh+MNUc6/GlEZTUCvUfNziY4ZWH0oVtyoArOu6jqlHp3Ix9SImiwmVQkVkdiRKQcnMpjPvu5rrrHXmq3Zf8f6p92nq25ThNYZjtBi5nnHdWjftrHVme5/t1jTr1oGtOZV4itG1R5eqE7+rUH53tf1eNEoN05tMv+89tBQWorC1xaLXk/T22xjj40l8azrlf96Bwu7xas0tRUUICgWCpqxaumgwkHvgILb16qL28sKYmkrW2rWYc3QobG1wGTAAbWjJhJMpPZ3CK1cwZ2aicHLCsX1767+xgrAw4l4ehSiK+MycgUvv3vcdjyiKWHJzUTg6Pvjf51NATjWXkZH5xxOXWcD8/bf5oFcNbDVSXfWYdWHsvJJM/WBXwmKyrG3bV/XmyxfqYqtRcjwinRdXnMbdXsOZ6e0frBj7a39TXaJUMxd1WEqLy46T6q3vxS0UbN2kFyyQBMh6LIC7tWSiKImW/VpZOz8dlrWGnDhpJeK1c5JYmcw/jn/zcykxMRF/f39OnDhB03tWQz766CPWrl3LzZs3yxxTWFjI2LFjWbt2LaIo4u3tzaBBg/j0009JSUnBy6vspNf9VrwDAwP/lfdU5p9NZr6BnguPoVYqmDegTqn6ZwCu/ACbRwIlr92iQsWIovEcsEirfBW8HNg7vpVU2rS4OaRchd6Loc5AqaRoXk2rTZZe40o13QLqh3hSxVNLyIXZDNIcRmMpxCSoeUP/Kh+oV+Em5HHH4s1wx6XsGt9K0h4xFkoWjcHNJO/oB7FnuiRw1vNLqV1BppS1pSkJwlLyU3hx54ukFKRQwaUCG3tsJDwrnG9PfcbIW8d5wz+AXlUHMrT6UDRKDXG6OAKdHrzSepebmTcZvHMwReYi6zZ7tT3LOyynpmdNzBYzq6+vJjEvkVDnUGafnY1FtNCnQh+2RGyhS0gXPmzxIfnGfC6lXaKZXzNMFhNDdw/lZuZNQp1DWdlpJVfTr2KnsqORbyNmHJ/BlogtvN/sffpU7FNqPHcDaKPFyIWUC9T0rImtyvah1yCKojXIu5NzB4PZUEpZ/O+CKIpkLF1G2sKFOLRqhbZSRTKWLLXudx81Cq83/1eqvSEqClNqKpqQEFQ+PgiCgDEhgfzTZ8jdv5/8o0dRurvjP+dzbGvVovDKFRT29qg8PEh4YzwFZ8+i8vEhcNlSEidMRH/7trV/QaPBfeQIAArOhVFw7pyUKVKM78cf49K3jzXothQUWPc59eyB96RJWIqKyPp2HYbYWEwZGRju3MGSm4u2WlU8x47FoXVrBOWTczaRU81lZGT+FRQZzSgEwareOu+XcILd7ehdxx9BEDhzJ5PR34aRmW+gnIc9Y9tUIF9vYucVadXt9XYVGbvuPHl6E22reLHoxXrWvlafiAagXVWv+wfdoginFksCZw2GQ/t34cxyacXinpl1QKq99q0Ndh6SrVZmFBAl1VS3nykdf2+QLQhSHfWvsfeQVFL3TIMmY+Sg+3dQYCxAq9Si/JPtwv7LeHh4oFQqy6xup6amllkFv4utrS2rVq1i6dKlpKSk4Ovry7Jly3B0dMTD4/7/n2u1WrRa7RMfv4zMHyUmIx9Xew1ONlLguu5UDPFZhYDIuqWfoKoqUN3fhUK/xqy7bmDY1ddRImKp1gdFqwlw4EOE8N0s9NjCj00H8dWhKNpW8UJvsmCruyMF3QD7ZkDlrnDhW+m5E9QU0sPRFmSwrr2RwAZ1OPblCAaodoMF8KyCssMH1Ewqzxt7HfhEtYyF5j6MaBlaIviptpV0RH6LTh9ZP1pEC3qNbalgUxRFZp6cSUpBCuWcy7G843LUCjWfn/2cc+lhnA0MJqUghe1R2xlRcwRnks4w+pfRDK0+lHF1x1nFuH7N5bTLvLb/NYrMRTT3b87oWqOZfWY2ucZca+CqVChZfW21VSgMoKpbVXqU78GWiC2cSzmHWqHG1caV1oGtpfEiEuocSmpBKl+1+woPWw/rPoD3m7/PmDpjcNSUtbFUKaSQSa1Q08i30W/fOyi1snpX8OvP5t5gv8w+s5miGzcpPH8em2pVsWvQoHh1+x1027cDkHfgAHkHJME9p+7d0e3YQcbXX2NTozq2teuQ+fXX5OzYgTk9vaTju4KwltLvRaakJGIGD0Hp5IQ5q3gBRKkEs1QCYEpO5k6fvmA2o/L0xOX5ARSGnSf/xAnSFy0u1Ze2YkUEW1uKLl8mpVhDJPmDDxALC7Fv1hTbevVJX7QI3bbt5P2yH4tebz3Pveiv3yB+7GsoHB2xq18fp27dcO7R/fFu8h9ADrxlZGT+liTlFPLlgQg2no3D1V7DsSlt0KqUNAh2Y9DK06w/E2ddtTaaRWr4O9Glhg9hMZk8u/gkAD5ONrSq6MGSQfW5FJ/NyJblrEH3vuspJN44SX3BSMeq9csOICsadr8Ft36Wvh/7AnJTJDEa0SKl6FXvI6Xo2XuAb52SVQB9nhSw6+Kh1WRw9n+8i/epIflgyzyUPEMeF1IvcCX9CpXdKtMuqB23Mm8xZNcQmvs3Z27ruX/1EP+1aDQa6tevz759++jTp2RlaN++ffTq1euhx6rVagICpNTW9evX0717dxT3sd6Rkfk7EZmWx0tfn6FBsBtmi8i2S4nM6luT5xsFoTeZWX1SUhof7naNGQVL4DZwG2yBF0QtSkFPkmsDBscM5v08X5r1XQ7zamCni2SQ0yWe7+eEKuIbME4uee4A5KfBun6QUmxb2XCkVI4U9g1NdHuIv6RjgFlqb+i1DE2d/giCwOhK0CD4Zd4/0o5n6wfQqfpv2yhaREupYDgqO4psfTbRumgWXlhI7wq9eb2epCtyJukM62+t53jCcTQKDfPazLOmgQ+pNoRzKedIKZBqwkfWHIlCUHAj8wZGi5EVV1ZwIvEEo2qOwiyauZx2mVCXUPpWlFLSgxyD0Bl00kp2y9k4a51Z120dqQWp1jRqgBeqvECeIY9TSae4lXWLF6q8QE2PmigFJWmFacTlxhHkVOKwoFVqmdVyFsn5yfg6lC1tAcpYez0JRFEk9dPPMMTE4DXhTbTlyz/6sRYLwq9+Hy16PaaUFNSBgdYgu/DKFVI+mYUhKgrXIYOxq9+AvAP7EWxs8XxtLKb0dGJfGoYhRvr/FIUCz9fHkXvwIEWXLoNKhfvLI8n5YTOmtDRsatbE79PZmHN15B8+QsLrb5Qag6DVovbxwZCQACbJlx2lEtsaNbBv3gyH1q3JXLMW3Y4dmLOyUDg7I+r1iEVFqP398Xl3JkkzZmJKSkKwtSVg8WJsa1RHFEVyftyCbs9u1N7eaCtWwqFtGzQBAYgmE9EDX6To8mWS3noLAPsWLQhY+CUKGxscWjQn+cOPKLoqTVrZt2yJY/v2KN1c0QQFo3R1IWvdd2R9/z0WnY68Q4fQlCv3VANvOdVcRkbmb0VYTBarjt1hz7VkTBbp5+ley5QTkemMXhuGrshkPaZrTR/mPFcHW42SyLQ82s05DECvOn7Mf74uJF2GuNPgUQn86oCNM7lx17BZ2RI1Zswhz6DsMktKAzcWwe4pcH6tpO6t1Eg1zzfuCYTrDpZS7/7COqG/C6kFqRSaCgl2Cn4q50vJTyEsJYxD8Yc4EHsAvVlKQ1YKSjZ038AnZz4hLCUMgH399qEz6JhwaAJ1veryer3X2XVnFzuidtAxuCODqg2y1s79Gfzbn0sbNmxg8ODBLFmyhKZNm7Js2TKWL1/OtWvXCA4OZtq0aSQkJLBmzRoAwsPDOXPmDI0bNyYrK4u5c+eyb98+wsLCCAkJeaRz/tvvqczfl58uJvDG+ovUDXKhbWUv5uwLp10VL1a+1JCtFxIYv+Ei/k5qjjm9g5B+k/PK2rh6B+CXsBetYCRNdKKr/hPScKVWgDNbxzRHcehjyVva0RfyUqVnTu0XpInf2JOS2viNbSWDCG4Og7dAzAlY27vU+HY796fz/5b/5nUk5yez4soKxtUdV8qS6VbmLd4+/jbTG0+njlcdbmfdZurRqYRnhVPfuz5hKWGUdy7P1t5bSS9Mp9fWXugMktjhm/XfZFiNYda+LKKFHlt6EJsbi7+DP9v7bLfWNW+N2MqsM7PIN+aXGld19+qs777e+v1i6kWquVcrFWg/DKPFaD1HzdWSYOioWqOsomN/JobYWArOnMGYlIzroBfLiHzp9u0jYZw0YSFoNLiPfgXXF14g/8QJUufMAbMFxw4d0FaQAnKVlzcqD3eyvl+PbscOnHv3wvuddzBERZGxahV5+w9gyc9HExqKQ+vWFF2/TsGpUw8cn2PHjhhiYtDfuoXC3h5NSAhF165Z9yucnQmYPx/7Jo0xpqSQ89M2nHv2QO3jgzk3l4ylS8n+YTPm7GxsatfC49VXsW/WDIVGg2gwYMrOBkDp6IjCtnRGRP7Ro6BUYt+oEYgiReG30YSEoHSwR3/nDhlLl+Hcpw/2jR8ti0AfEcGdPn0RjUacunbFb9YnperIRYuFvEOHUHl6Yluz5n37uLvyX3DmDHb162Fbu2wt/OPyqM8mOfCWkZH5y7j783N3xnbvtWRGfxtGcbxNo3JuvNmhEpW9HUt5nEan57PxXByejloahrhR3XgF4cCHkjJrcDPm77rIyZNHeeeVQVTnjuRfffchb+Ms+YIeng3h99h/3LUuObcKTiyQtpVvK6WX+9SS/E0vfivZeg36EVSP9jLwT+d21m1+jvqZETVHlEm/yzPk0WtrL3pW6Mkb9d4gz5DHjBMzKDAV0Cm4Ex2CO+CgcSjTZ6wulnU31tGnYh+rGA1IFiRKhZKMwgzi8+Kp5VGrTMrcjqgdTDs6zfrd38EfrVJLVE4UVd2qYrQYiciOAKSXwQupFzgYdxDA6mcK4GnryY4+O7BTP55gzOPwX3guLVq0iE8//ZSkpCRq1KjBF198QatWko3QSy+9RHR0NIcOHQLgxo0bDBw4kFu3bqFWq2nTpg2zZ8+mcuVHr3n8L9xTmb+OVF0Rp+5k0qm6N1qVEnRJ0qRt1R58sjucpUeiGNQkiBcbB9Nl/lG0KgUXpjbDZutwMnLyyHKtSaXw5Vi0zphfv0SGyZYFWw/TKO8ANx0aseSGDRW9HPju5SZ4Omr5+pcwBp/shspceM8o7v7miTD+KoR9I42h8StQuZuU1ms2waahmKOPIxRmc9xSnaTua+jfWArcCowF3Mi8gVqhxtPWs9Tq7sCfB3Il/QrDqg/jzQZvMvHwRCq5VuJ6xnX2x+7HVetKkFMQV9OvYhbNOGocWd15Nf2398ckmljQZgGtA1uz5voa5obNpZ5XPVZ0XFGmtGdv9F7eOf4OHzT/gI4hHUvtSy9M56uLX3E+5TxOGicCHQNpF9SOtkFtf1P4SjSbyd60iazvvsd10Iu49u9fps2xhGN8E7aMma0/eqR68vth1ulImT0b25o1cRkw4L7j0t+5Q8qsWeQfPmLdpg4MxGfmTPJPnMCcmYnLc/1ImDQJU2ISKj9fTIlJUkOVqmSl+BFQBwdhjIsvSecWBKkc7h6ce/XErklTMr/+GlNqKnZNmpC3fz+i0QiA0sODchvWo/LzI2vNGlJmf4omJITARV+h+Y3JT4tejzk9HZWf318qTgaSoJohNg7nXj3LZAP8VfzlgfeiRYv47LPPSEpKonr16sybN4+WLVs+sP1XX33FwoULiY6OJigoiOnTpzNkyJBHPp/8MJaR+edxKiqDz/bc4vV2FXG31/Ds4hPoTRY6VvNmfPtKVPN7wL/lKz/A/veg9TRJdGbdc5I9l62b5GX902uQeg08KkNBuiQO414BDAWSCJrWGfQ5kor4wI1w8GNJBM29ImRGSqnk/VZJFl53sVik2m3f2v+ZoBug649dicuNo0doDz5u+XGpffPC5vFT5E90DunM6/VeZ/S+0ZxPPW/d76RxYlStUbxQ5QXrqsXF1IuMOzCObH02/g7+bO21FZtii7WpR6cSmR3J7azbmEUzXcp1YUaTGaQWpBLqIimc3sy8ySenP6Gqu1TLV82tGikFKbxz/B0mNJhARZeKzDgxg22R26jsWpm2QW1Zf3M9zlpnonXRuNm48WzFZ6nkVonOIZ0ByCjM4EzyGbqU60JCXgL+Do9ZGvAA5OfSk0e+pzK/hdFswWwRS+qZH4PeXx3nYlw2AxoEMrtfLVjTG6IOQos3GXSnPb1jZ9HV/ha25ZvxcXgAq3MbcLjCenzjd5XuqN0MaDmh1JgUgsDZ6Exq+jtzITab2btvciUhh1eV25is3oDQ7DVJqPP6Vukgn1ow+uhDx7vraiJv7JyPxWzLsbGT8XGSJhIPxh7k9YMlVpNv1HuDPhX68Oy2Z8koygBAo9AwstZIFl1chEqhYnOPzYzZP8ZqsQXQPqg9ExpMIMAxgFf2vcKJRMkq8KXqLzGhwQSyi7Jx0DhY65//KBaDgfixr4HZTOCSxWUUsc3Z2cSOGGldrVXY2VF+/y9lVphT58whY/kKAleuwKF588cehyiKJE6YgG6n9PfqNnQoXlMmW4M8URSlwPXzOWA0gkqFbe3amJKSMCYm3rdPtZ8foT/vIPeX/WR8vQr99RugUuExejQ21auRd+Ag5uxsRIsFY3w8xvh4bBvUx7FNW1LnzMGSK6nfO3bpjNvgIWgrViBn2zaKrl/Hpnp17Js2RVuubB157sGDUpq4Uknw2jWlVoFNGRkonZwQ7mPlKPN4/KWB9930s0WLFtG8eXOWLl3KihUruH79OkFBQWXaL168mClTprB8+XIaNmzImTNnePnll/nuu+/o0aPHI51TfhjLyPwz2Hc9hYpeDoR42DNoxWmORaQzqEkQM7pXZ8KmSxQazCwZVA+VUiHN5l7eKFmX+NWRvh+dAwc+kDrzqCytUs8OAVPhg0/qWwde2iEd/01XSL4ibW80Crp+Jr3sLGkhKZQD1BoAfZf9eTfhCWMwGygwFuBi4/JE+43MjqT3T72t3w/2P8jxhOOcTDpJ64DWTD82HYPFwII2C3CxcWHknpFolBqer/I8v8T8QrQuGoAQpxA2dN+ArcqW57Y/x62sW9Y+x9YZy+jao8ksyqTdpnaYLNIKwN3VaZVChZPGiV/6/YL6Yeq795BdlE2bjW0wiSa29tpKkKP03DmbcpaaHjVLrdynFaTRfUt3isxFfNj8Q2Ycn8FzlZ9jSsMpf1icTX4uPXnkeyrzMExmC53mHcFsEdn5RkvslCJsH4/FqyoXA17EQ1FA0KHXoUo3SfDyV3x3Opa3tkjPh23DKlNrfUMQLYgIHKIBbThbqn22aI+LkA8KtVSSdHMHOAXAa2dAY//Acd511ABw0Kq4Mq0pgo0TZEZR8FVj7MwGaWK59dSHXu/W8B28c1LKAGoT2IaPW3yMg8aBHH0O/bb3w2KxkFqYSgWXCkxqOIlX9r1CiFMIXnZenEk+Y+2ne2h3Pmn5CemF6VxKvYSIiK+DL9Xdq1vbrL+53upf/fkzn9MppNNDx/Z7SJ03z6qm7T19Om6DB5Xan7bgS9IXLULh5ITSwQFjYiLuo0bhMXYMBWfPYVunDkXXrhE7dCgAjp07EzDvi7LnmT8fQ2QUvh99iNKxrJBazk8/kThlqpRdULzC7DpkMD5vvYVoMJD8wQdkb/oBAPtWLfGeNg1tuXKYsrJIeP0NCs6dw75lCxT29uTukjLr/L9cgFOHDoAUuBddv46yOO37t9BHRZH1/XocO7SXUrYfE0N8AoJCQO3n99jHyjwaf2ng3bhxY+rVq8fixSWKdFWrVqV37958UqxEdy/NmjWjefPmfPbZZ9Zt48eP59y5cxw7duyRzik/jGVk/h5EpuXx4/l4nm8YRKCbHTEZ+fg426BVKUnOKaL93MMYzBb61Q/gu9OxKBUChya2JtDNDotFxGA0YKNWSw+8S+thyyuSrdbrF6WXmp/GFp9JAETo9ImkAG7nIa1UF2aCkz8M+BYiD0jq4u3fAwdP6bDcZPi6i2SnMvo42LtL22/sgA0vgoMPjDkJdm73ubq/H2aLmeF7hnM94zrfdv2WSq6VWHJ5CekF6bxe73VrDd/Z5LPU8axjDV433NyAs9aZzuU6P7DvOefm8M21bwB4pdYrBDsF89axt0q1aejTkJUdVyIIAicTT2KrsqWOVx3MFjPbIrfx5YUvaRnQkveaSV7MR+KPsDl8M60DWzPjxAwAVnVaRUOfhkTlRHE57TL1vOqRWpDKhMMTyCzKxM3GjRUdV1DRteIj35dx+8dxMukk7zd7n66hXX+z7aH4Q9bv3UK7MavlrEc+14OQn0tPHvmeytwlPU+Ph0NpjYY76fm0+fwQAB/0rsFgz0hY2wcjKqoWraKv8iifqovroJ//Hqrc89tgLASzgSlbb7HhYhoT3E8wLn9hmfOa2r2HylyE4fRKNIWpAEQ2/oDyXV6HzDugcSh53jwAo9lCxenSaqpSIRD5sTSO9MJ0ev3Qma4GkQl9NmLj9mARLqPFSO+tvYnNjQUg2CmY9d3WlyrvySrKotUGqfRjaLWhrL6+mo7BHZnZbCZrr6/lTNIZsvXZzG8znxDnkIeOOb0wnR5beuBm48bW3lvv60X9Ryi8eo3oAQOsStRKV1fK79uL0kG6HovBQESbtpgzMvD/Yi6CVkv8mLEIdnaSyFdUFCpPT1AqMRW7LSjs7al48gSGyEgKzp7D5fkBFJw9S9yIkQDYN29OwIL55GzfQcG5c+gjIzElJ2POzgZRxPON11H5+pI0VZrcCFq1kuytW9Ft2w4KBd5TJuM6ZEip1GtRFLHk5VkD+sIrVzFnZ+PQssUTvV8yfy/+Mjsxg8FAWFgYU6eWnqXr2LEjJ06cuO8xer0eGxubUttsbW05c+YMRqMR9X1SIO7n7SkjI/PnE5mWx5QfLlPD35mpXaqUSuc7GZnBqDXnyNWb2HI+gU+ercWEjRdpHOrOgufrYrJYqBvkwtHb6Xx3WnpZ6F3Hn0AXGzj4MYob27FJvy2JoL24EfYXr2wXZsEv75YInLWeBgnn4fYeKU0coHJnaDACLn0PTceCawj41yt7AY4+MOaUtPqtvud3p2p3ePkgOHj9LYPujMIMVl5dybnkc7zV+C3qeNUB4IfwH6zp3fPOz+PV2q+y6OIiAI4nHufN+m+SXpjO7LOzaeHfgi9af8GR+CN8ePpDtEot2fps/Bz8qOFRAwe1tFLiaeeJ0WJke6R0v+e1mUe7oHYUmYrYG72XXGMuV9OvIooiExtMtL50NPUr8XNWKpT0qdiH9sHtS11Hq4BWtPSXyo42hW/iSvoV3j/5Ptt6byPUOZRQZymlPMgpiM09NxOeGU5Dn4aPvNp9lymNpuCidblvjfmvGVt3rDXw9rT1ZFqjaQ8/QEZG5i9BFEVyCo28sjaMKwk5nHqrndXOCyAyNc/6ee3JaFr476McoMZETXUSNYku6ezHUdB7ESkWJywnF+GTsBcBkVkqO5Q2w6ipOwZKoMWb5F/4Afv8WNbYDGJIy/EACE1f59M576MzCIxqKHkO4/ZollFqpYIAV1viswppXK7kebPyykp0Fj3XfGqidQ19aB/rb64nNjfWOjH5zbVvuJx+mWZ+zaxtXG1cqeJWhZuZN/n2xrcAVHarjJPGibF1xjK2ztgHdV8GD1sPtvXehkapeeJBt2g2kzR9OpjNOHbqhD48HEOx8JbXhDcByN21C3NGBipvbxzbtweVCm21quiv38AQFQWCgCktDQCVny+i0Yg5LZ38o0dJ/uBDTMnJ5B0/hjE2znre/OPHCW/eArGoqMyYHNq2xX3UKASlkqLLl8n67nviXh2DqNeDUknAlwtwbNu2zHGCIJRaRbetWeOJ3iuZfzZPPPBOT0/HbDaX8fH09vYu4/d5l06dOrFixQp69+5NvXr1CAsLY9WqVRiNRtLT0/H1LSv5/8knn/Dee+896eHLyMg8hGO303lz40VSc/Wci8niZGQGwe52LB1cH0EQ8HOxQakUUCkEEnOKGLpKSmW7k5ZPvsFEgKsda4Y3YtulRD7YcR290cLY1qGwaxKcXVFyotRrsKgp6HWgtpeE0cK+lvZ5VJZq5y5vlAJvg1T3RIUOUqB9v2D716geoGT9KMf+idzPfzNWF8v3N79n8+3NFBan0++8s5M6XnXIKMxg/oX51rbHEo4xvMZwJjWYxGfnPiMhL4EJh0vqDN1s3FApVLQJbENL/5YcTThqTR0UEHDRulDOuRxfd/6aY/HHyCjKwM3GjVYB0oqJjcqGL9p8gVJQojPoKDQV/qb1yv38UO9e42fPfMbSS0sZXG3wfcVaPGw98PD/fT7mAY4Bj9y2ilsV+lTow46oHbzf/P1SSr8yMjJ/Hy7F5zD8m7Nk5hsA2HI+gaHNQqz7I9NKAm83ew0Ft0tqpNd102IJS4c0yBHtcTbkwsbB/Np1XjAV8IFiOWZRWnndr22HosML7D54CHVIE2s7tdaO0W9+gMks4mb/+Lof60c14auDEbzWVsrkSc5PZsOtDQC8Vvc1BEEo9Uy41+rLbDHz6dlPAcmqq6JrRT5q8dF9zgKNfBpxM/Om9XruFbR8XDztHr6S/3vJ2bpVUtx2dsZn5gwKz58n/rVxZCxfjiEmBue+fchYvRoA1xeet9Yk+0ybRvz4/+HQogVeEyeQvXUrefsP4DV5Mjlbt5K9cSNJM2ZizpDq2u+KoCk9PPCeNJHEqdMQi4pQ+fri8lw/bKpUQe0fgMrNFaWHh/Xee02YQN7hIxgTpPp3n5kz7ht0y8j8Fn+aj/evX6IeZuj+zjvvkJycTJMmTRBFEW9vb1566SU+/fRTlMr719hNmzaNN9980/pdp9MRGPj7lAtlZGTuz72q4xl5egatlGrSQj3tyco3cCsll1spuRy8lUrbKt4Eu9uz8ZWmaFUKBi4/TUJ2IVV9nVg3srF1VUKwmOhV2Z7ONdpSaDDjcvrz4qBbgC6fgm8t2DBI8i4F6PY5XFgHMcVlJ11mgVINlbtI4mgWEwhKCG39lO/Ok0UURd47+R6+9r6MqjUKQRBYfW01c87NsSpxV3CpQGW3ytYV2XU31pFryKWqW1Wqe1Rnz509pBakMqT6EHpV6MWcc3O4mXkTgGZ+zRhXd5z04ibAp60+ZdnlZdzOvk1yfjIR2RFk6bNQ6BQk5CXQwKcB7zR5B7NoLrW6cVdEx1nr/IcDVH8Hf95v/v4f6uNJ8W6zd5nSaAr26gfXZcrIyDx9Dt1K5evj0bzbszq3knXWoBtgyeFINoXFoVEqWNdJQBF1DfBjUJMgPuxeCXHWbWtbu7RLiFnS72F+v3U4Rf2IkBBGXmo0h8w10LadQocWzWHjYJS396IU4JYlgBU3VHz3ckXa1KlUZmz3rrY/LgGudnzStxYFxgLWXFvD+lvrMVqMNPBuQI4+h0E7B9EjtAfdy3dn+eXlXEi9wNedv0YhKIjKiQIk/Yz+lcuqet9Lc7/mXEq7xKW0SwBUci17HU8DU1YWhjt3sK1bV5pUMBox5+aisLUlbb7kJOLx6mhUbm44tGuH+8gRZKz6mty9e8nduxeQrLhc7lExt2vYkErHS0pSPV5+GY+XXwbAkqsje+NGa9Dt8lw/crbvQCwqwnvSRJx79ULp4YE5IwPHzp1RaB48eaKwt8fvs09JnDoNl7597qukLiPzKDzxGm+DwYCdnR2bNm2iT58+1u1vvPEGFy9e5PDhww881mg0kpKSgq+vL8uWLWPKlClkZ2ejeASpeLnuS0bmybP9UiLfnY7lvV7Vicss4NV15wn1sGftiMboTWYWHoigZoAz/RsEolaW/nealFPInqvJ9K7rj4udBop0UoB9egkU5UiWXiotLG8LiND9ixKxm6TL8G1fSYn8pZ8h5Sp83RWq9oA+S0pOsraPVMcd1BSG7+afhCiKrLm+hjaBbQh0DGRu2Fy+ufYNCkHBxu4bOZF4grlhcwHpxWlwtcE082tWagLzTNIZ3jj4Bss7LifAIQBBEH53MJxakMrtrNvU9KyJk0b+DX0SyM+lJ498T/9bXE3Iod+SExQZLQxuEoxKKfD18Wj6Nwhgx+UkCgxmQGSs8icmqTcCcKLxIlzr9KCq8TqsukcAzNZN0gDROMDUOFAouJ2SS4cvjqBWCpx7uwPOtmrIz8CytCWZeUnEBr9MtYGf/C6FdJB+54fuHkoFlwqMqTMGdxv3Ur/hyfnJjN0/lvCscEBygljRcQWnkk4xN2wuFV0rUmAssCqNL2q3iJYBUqmOzqBDq9SiVT4gg+sermdcZ8COAThrnTk64OhTsYPK/HYdafPmoSkfitrbh7yDBxGNRtyGD8djzBhiRwyn6NJlVL6+mJKSUPv7E7prZ6kAuOhWOOkLF0pK4YKAc+/euA168ZHOb9HrCW/aDLGgAE1wMKE7tmOIjcUQE4tDm9Z/uSWWzL+Lv6zGW6PRUL9+ffbt21cq8N63bx+9evV66LFqtZqAACk9cP369XTv3v2Rgm4ZGZknS0RqHt+djmXTuThy9SZ2XklifPtKXHinA1qVQlIcB2Y9W+uBffg62/JS8+J6t9QbsH6gJHR2ly2jJEE0RKjZv7TCrG8t+N91aUVboZAsvKbEwK9VppuOhfgwSZ38H8bqa6uZEzaH9TfX4+fgZ1WYfbfpu1R2q0xKQQoqhYpRtUbxau1X79tHXa+67Om354kEyl52XnjZef3hfmRkZGSeBCazhde+O0998yXe0PzIlzfHYnaXPN8bBLvRtLw7+66n0CthLp0KdliPa3b9A2jdFc4V6wp5VYPU61LQDZJNV/G75d7rKQC0qOAhBd0A9u582agfKyK38FWrdr8r6DaYDRSZi0jITeBC6gUupF4gRhdDsFMwM5rOsLazVdlSZCrC3cadMXXG0D20O3ZqO+zV9swNm8vtLGnF3t/Bn6mNptLCv0Sg63F+900WEw28G+CsdX7sgFM0GEic9haCrQ2+H3zwSMdnrllDyseSmHLRpcsUcblk36pV5O3fjyEmRhpbkuRr7Tl+fJlVZ5vKlQj4csFjjfcuCq0Wpy6dydn8I54T3kRQq9GWL4+2/IMF62Rk/mz+VDuxJUuW0LRpU5YtW8by5cu5du0awcHBTJs2jYSEBNasWQNAeHg4Z86coXHjxmRlZTF37lz27dtHWFgYIY8gsw/yLLiMzJPAYLKw6FAECw9EYLJIPw01/J34YXSzB798JF6EbeMke5ZWk+HUV3B0LgQ2ggrtJZXX86vBkAfOgfDMZDj8GeRI4mqo7WFcGDiV1XL4J/HrcpqonCh87HywU0u+qumF6Wy5vQWDxcDYOmNJL0xn8M7BxOfFA6BVapnccHKptMG7L2oy/0zk59KTR76n/wy+PRVDZFoe73SrhkLx+1cWw2KyUH73LHX0YfxkbsYbxtcA+Glsc2oHukBCWHHWlABdZsOZZZARAVV7QkGmVKLU6WNJhNMg1X9n1xrJty6jcXfQsvNKEkdvp/Nuj2rWieIiUxEN1zUEoLxzebb23vqb49Sb9STkJhDqIgmibY/czlcXv6KWZy123dmFu407GUUZ2KvtOdj/ILYqW+ux0TnR2KhsyuhlDNgxgOsZ1+kQ3IF3m737l2Uipc6ZS8ZySQk+cOkSHJ555qHtc7bvIHHSJADcRgxHW7Eixtg4HNq0Ju/IEdK/lJTiFXZ2+M+fX1w3LeLSv7/VJ/tJYdHrMaWmopFLUWX+ZP6yFW+AAQMGkJGRwfvvv09SUhI1atRg586dBAdLL5BJSUnExsZa25vNZubMmcOtW7dQq9W0adOGEydOPHLQLSMj82gUGc1ciM2mYYirddX6Lqm5Rby8+hyX4nMAaF3ZkyFNg2lV0bOkbX46KDVgU/yjYiiAzSOkF53ky3DlB8gorqkL3y39uUtIS3hutWTf5RYK33QHRHhm0t866BZFkYS8BHztfe/r67w3ei9rr6/F39G/lAXV6H2jydHn8Hq917FX2zPrzCzyjfnYqmwZWm0oHrYeLOuwjIlHJuLv4M+b9d8sIwgmB90yMjL/BBKyC7meqKNDNW9EUeTtrVcBaF3Zi2cqFQtymY2we6rkONFsXNlOUm/ApmHQYjzUfh6A+kEuiEIkAB0V57CnECehgKrpe8l1b0fW/hkEAdQaQGToi9xM96RrxnCEG9tK+g1uDj41IfYkAHc0Ffh8bzi1Apy5nSIF403Ku1ubm0UzLfxbcCzhGKkFqZgsJqu2xf2wiBbeP/k++2L28XGLj2kf3J69MXtJyEuwpoi/3eRtPj/3OQl5CRyMPUjncp2tQmkPsvFa0GYBd3R3aOzT+KmnRYtmM8akJPTh4WSsKBE+TV+yFPtWrR44HmNKKskfSG4kbiOG4zVxYqm2NjVqYM7KJnffPvxmz8K+SZP79vOkUGi1ctAt87fiTxNXGzNmDGPGjLnvvm+++abU96pVq3LhwoU/aygyMv858vUmfryQwJk7mbzRriIVvBwQRZGX15zj6O10Wlf25KuB9bDXlvwE/BAWz6X4HFzs1CxqqqNpQApClUYlnSZegFVdQDRDxY5QqRPEn5WCbjt30OcWB90CtJkOZj3En5PqtAMbQ/XekigaQEgL6LlAetFqcv/fib+SfGM+l1IvcTblLHui9xCXG8fAKgOZ1ngau6N3s+D8Aso7S+lqd22oYnQxVtVZvVmP0WKkwFTArDMlwXhVt6oMqjYIjVJKpwt0CmRD9w1P/fpkZGRknhSiKNJ81gFAUuqu6FVi4ReenFsSeJ9dCWdXICIwI7w8N4tccLfXMqBRIG0qe8Hx+ZB2A+O+90kN6oG/qz1kRiEUZQNgKxjoqzzKq5pdaH5KYXxAEKeVIls1WgJbT+X4rXRmHNXyv8B3eUO1BZIuSllWPjXBt4418HYJbQTHkrlcPMnsaqemkleJ+4K92p6FbRfSakMrdAYdV9OvWu0bMwozSC9Mp7KblPJutBh598S7bIvchkJQWMUZ3278NlHZUcTmxuKodqRVQCtuZN5g2eVlfHjqQ5ZfWU5L/5aMrDXygSvZ3vbeeNv/WnP9z+Fe4TNTahpxI0eiv10iTOfYoQN5hw9TeOECBWfOYt9Yejcw5+SgsLNDUKsRRZHkGTOw6HTY1KyJ1//+VyZAFwQBn7en4z39LbnGWuY/yZ8WeMvIyDx9zBaRzWHxfLb3Fmm5ks9943JuVPByYOO5OI7eTgfg0K00eiw8Rk1/Z+Y/XxeA0a3Kk11gZEglEwHfvSCphQ/fA0FNwFgEW0ZDsZ0VN3dIf+7SZxloHeD4Aqg3WFIc/y3qDXmi1/64GMwGDscf5qeIn/ii9RdWn+jDcYeZfGQyBaYCa1sbpQ1j6ozhZOJJph6Zilk0E5creYGqBBXDagzj2UrPWlcwtEot+5/bzw/hP/BF2BcUmYsYW2csw6oPu++quYyMjMw/lfOx2dbPbvYaErOL8CcNR6GQjtVbSzsKMuGQVPMrIOIVsYG1Jqms5mRUBkf/1win69IqtTovgTc/Xcxz/Z6nn+pcqXPNVK9FJZoRgaNqqTdj9T4su2rh452SYnleSEfo+gak3QQbF0kbxLc2IiCobPGrUBOlIgWzRWTjK03Rm8xl0uGVCiXN/JqxO3o3RxOOUserDnqzniG7hhCfF8/33b6nmns1ph6Zyt6YvSgFJR80/4Cmfk0BKWhe1WkVs8/OpqV/SzRKDUOqDeF00mkupV0iNzuXpPwkhtcYzl9N4aVLxL32Gua0dGzr18eckYEhOhpUKgSNBpvKlfGb9Qmpc+aQ9d33pC9ahF2jhhReuEjssGHYNWxI4PJl6HbuJO/wYQS1Gr+PP0JQPTjEkINumf8qcuAtI/Mv4URkOh/suMGNJB0Awe529K0bQMMQN5Jzivhwxw2eVRzhLfufmGAaw6G08qTp9NbjFQqBt7pWhY1DpKAbYN9MSS380MfSS4y9F/RfDZEHIeY4JF+B+kOhYnupfdCfmzb2ODzIwlAURb66+BXf3fyO3GIP8E3hmxhYdSA7onYw7ahk1eVn70d97/q0DGjJMwHPYKe2o5p7Naq7V8fPwY+q7lVJykuib8W+VHWvWuY8CkFB/8r96RTSCb1ZLwuXycjI/CvZeFaahHy2XgCVvB3Zeymazdp3cRdyUSu6APZwaBYUZYPGEQy59FceIqbmOM7H5ZGYU0jiqR9wMuZb++yjPErjcmPgVJi0oUp3uLkDFZIXdWqPL+DqFyhFCGz9Ds7hFuux5T0dMIompt/8GjcbN6Y2mkqKXy2eDwqkstaNL1UCwe52RKXlYzBZaFmxxJt6f8x+TKKJ1oGtaRnQkt3RuzmWcIxxdcfx7fVvic2VyiS33N6CiMjemL2oFCq+aP0FrQNbl7ov3vbezG091/rdWevM152/ZsH5BXx/83veqPcGLjYuf+jem7KyiHlhIIJajfsrr+DUpTPCPTa8+tu3SXpnBrZ16kh2Wtu2k3fwICovL9QB/lh0OnJ/2Y9okCzaCsOk+63y8yV4zRo0ASXlT27DR5C96QcKTp8m67vvyPr+e0S9nvxjx9Bt20bqF/MAcB/9CtqKFf/QdcnI/FuRA28ZmX8Be64l88pa6YHpaKPijXYVGdI0BI1KWoHNzDfQoYor029vxM2YyQqXr/mhw0ZcnBywWMSS2f74c3D9J0CQrL7iTsG6fhDxi7S/x3wIbib9+ZtxN807RhfD52c/JywljP6V+9MxpCPV3KsBUtC95PISll5eCoCXrRc9K/SkW2g3LKKF6xnXAege2p33m79fyr8apBen5R2Xo1aqy+x7EH/U61pGRkbm74LJbCE6I5/yng4IgkC+3sSOy4kADGgo1dLa3fwBHyFLOiB8D1TvA+dWApDZbSmWH0fjLWQzNTSapOYd8HG2wXPrCwAkuzXEJ/MsPdRnsHcUIKF4xbt6H8mGMvoo1H+JaL/qcBUCnINROflTL1hnHWOwuz37Y/az684uAJ6r9BwHU06SrhTQaG1RK9RU9HIgKi2f26m5tKjoYT127Y21hKWE8U6Td2gb1Jbuod1pFdCK9MJ0ll9Zbm23O3o3ReYiADoEdygTdD8ItULNhAYTGF9vPGJGJrqdO3Fo2xaFjc1j/k1IZH6zWlqdBhInTiRj2TK8p0+3poKnfj6HwosXKbx4kcx7yjz14eGl+nFo0waviRPIWLYMY0IivrM+KRV0A2gC/PH83/9I/fRTUj74sNS+xGlvgcWC2s8P9xEjfte1yMj8F5ADbxmZfwE/XZQEXHrW9uPdntVxsy9tyeFmr2FujVi4JdmpqLKjeD7/W9CWgwPR0PQ1ybpr50TpgDoDwcEbjs0tCbpbTYIqXZ/WJf0mRrPRmh4OMHb/WMJSwjCYDZhFaVVk5dWVrLy6ko3dNxLqEkqnHzqRUZQBwOSGkxlYZaA19TvfmE+OPodxdccxsuZIa9r4r7mrUi4jIyPzX2PZ0Sg+3X2L6V2r8nKrUH6+nES+wYyPkw1mi8i+a8k0iltnbZ94bhvKPBPeFhN6r9pctmlIpNCGEfyEx5UVeNTrDfmpEHUQgNmq0UwQ7xBAOlzbAslXuKjV8E3iHia0e4vAxCtQbwjRkVJaeowuhu5bujOwykBqBwSTrCuiVoAzrx74rtS4d9+RhD5H1hoJQJFRWiGfte8IF/XzGFRtENXdq3Mp7RIATX2b4mHrwSctpfT4o/FHEUWR6u7VSSlIIb0wna0RWwEpsH8cLAYD6XO/IGv9esSiIhw7diRgwfwHtjdlZmJMTMKmWtVSqt9mnY6sddK9durRg7zDh9GHhxM7dChuQ4fg/Oyz5B0+DIKATbVqFF27hrZqVdyHvYSlqAhTcjIKJyc0QUE4PPMMglKJ3+zZDx2720tDyTtyhIJTpwDwef890r9ahClFsmXzmjTxd08iyMj8F5ADbxmZvwGJ2YXsuprMM5U8qHCPyMuvySk08tHP1xEQGNYihMrejgiCwMIX6vFDpXh61fVDq3pADfFpaZWXgEYQfwaOzyvZd2Ed2LpAejhonaDNW6B1hOtbwZAPvRaVpJP/xRjNRnZE7WDxpcXMbjWbul51MVqMhKWEUVhcg97CvwWdQjqx8spKEvISiNHFkGPIsQbdg6oOYnC1waX6tVfb81GLj5769cjIyMj8U/h8zy0APtp5g2frB7D0iKQ43qicGy8sP0VX26t0EKNAoQaLEbfU0yTnShO+h9Ut6FjZi6Zj30Vcshsh9iRseQVSroFoIc6hJltibQlVtWCcaitsex0sRub6+GM2FVDo4AGNXwEgWhdtHVNsbiyH4w+z4ZXFiCLcyb3FhdQLqAQVe/vtJdeYy62sW6gEFR2DOwIwpJk/R+POYReyhgNx+dzKusX0xtMxWUz4O/iXcZhoGdCSn/v+jE6v42jCUXQGHUpBSWR2JA28G5S5T2adDoWtLYK6bGZUxpIlpVafc/fuRbdrF05dymqjWIqKiB7wPMa4OFReXrgOHIj7K6MQBIGs777HkpeHtmIF/GbPwpyTQ9qCBWSv30Dm6jXk/rIfAMeOHfGf9wUWnQ6Fk9Mfqq8WFAr8Zn1C3MsvY1OtGi7PPYfCzp7EiROxa9gQx86df3ffMjL/BeTAW0bmb8CxiHQ+2HGdTxQCXw9rWKrm7Pszsey4nEjLip5sOBvHnXSpDm7DuTjmDahD77r+KBQC/Rs+xDIj4bwUbCvUMOBb2P66ZPXlVl4SnkkPl1YdHH1h4EZwLn7pGHNKOuYJe2s+LqIosvPOTg7HH+ZM0hlrAP3Vxa9Y3mE5aoWaff32odPrEATB+tLUs3xPCk2FVqXZ+W3mE5cbx6Cqg/6ya5GRkZH5J2KxiNiolRQYpIyi2btuIgIeDlqmdKnCtkuJ9Df9DEqg0cvkXdiMgz6FkCKphCczWAostZ6h8NzXsGEQXP1B6tvBh2EZ0mToUa8XGeeYDjHH0AtwQ6OiKP0yAiUB493Ae2i1oay+vpqzKWcxiYU4aBz47oa02t0hpAOedp78cFE6RzP/ZjhrnUnIS2DO9ZexD4mjWM2EhLwE1l5fC0AT3yb3DU49bD3wsPWwenUD5B0/Tt7hwzi2bg1Iz6qstd+S+tln2DVsSNCqlaX6EM1msn/cAoD3229jzswgfdFikt//ALtGjVC5u5dqn/nNNxjjpBp6U2oqafPmoa1cCbuGDclcvRoA91GjEBQKVK6u+M6ciSY4mNRZs4v9scF95AgEQUDp/GTKntQ+PoRu32797ty9G9rQcqiDgmXRNBmZ30AOvGVk/gZ4OmipE+jCxbhsZu++SYsKHtYHmIutmuMRGRyPkIJNfxdbagU4s+daMqfvZNK7rv/DOzfpYdcU6XONvuDoDf3XQOYd8Kws7T/yqRR8d55VEnSDVOf9F5CSn8K7J9+lnHM5JjWYxPpb6/n49MfW/V62XgyqNogBlQdY75Oz1rlMPfW99i4AbYPaPp0LkJGRkfmXEZNZYA26W1Xy5MUmQbzfvQJxcTH4u9jia2uhmUXy76bBcAozM3EIXw/AVSrQtmnDks6qdIOeX8JPr4FbKIrBW5hX4MKiQxEMahIMQZth8wguRe+jCBEPWw8quFTAYDZgspiIz40H4JnAZ/gl9hcS8hK4kn6FOl51rLXdA6sMRBRFFl1aBEDnEGk11sfOBztVSclQ68DWvFzzZd49+S4gBd6/hSiKZCxZQtr8BQAEf7sW2zp1SJg4idzdUlp7wblziBYLCAL5R45gU6MGRTdvSinezs64PNcPQRDI3X8A/a1bJE6bRuCSJdZ0cmNKKunLpLpy348/pujqVbK++470hV9h16AB5qwsNCEhZVbK3YYOxRgbS9Z332PXpAm2NWs+wt/uH8OmWrU//RwyMv8G5MBbRuYv4t1t1+hSw4fGoe60qeJF7UAXWs4+wNUEHXP2hjOxk+QT2qy8B293q8r+G6k42qj4pG9N3B20ZOTpUWXfgfl1QKmBCu0l7+ysaAhuLqXkaexh12RptVvrDM8UB+AqLXhVkT6rbaDdjL/kHgBcy7jGdze+o0u5LjTxbUJiXiKj9o0iIS+Bi6kX6Vm+J1+e/xKAZys+S+dynannVc/qhS0jIyMj8+cw9rvzxGTkM7FjZbIKJOXr2oEurBkuiXex/Q0qhK2GIT/R3iEaTa6ZeNGDkWsT2di6ExQH3ppaffBy/FXtb91BENoa7D1BpaWGKyx6sX7J/gHfcurE+xDxA018mxCWEsa7J9+lV/le/NjrRxLzEvG286aaezUS8hK4mXmTQMdAQl1CySrKorZnbU4nn7Z21yawDSBZhb3d5G1e2fcKA6oM4M36b5JemM7tLMm3urFv49+8L2lfzCNj2TLr95SPP8GuaRMp6FarwWxGNBgwpaSgj4gg7pXRqPx80ZaTVsudu3VFoZUmtv0+/ZTo/v3JP3KUzFWrcB8p1aGnzZ2DWFCAbZ06OPfpjUPrZ8jeupWia9counYNQPLD/pVtlyAIksBay5bY1q79m9ciIyPz9JADbxmZp4TBZGHv9WTqB7tyITabb05E8/2ZWE5MbYu7gxY3ew3Dmpdj4cEIFh6MIC6rgHkD6uBsp2Zky1BGtgwt1Z+7DfDzKMi6I21Iv1WyM+IXOLFAShPPTwUE6LcK3Ms/tet9VKq4VuF21m22FYvl3Ms3nb+h0FSIjcqGQKdA3mnyjuyDLSMjI/OUuBKfQ2xmAS99fZY5z9VmxZAGJZVHogg3tgMiXFhLi+IA8JSlGtGZBThWbYd5hw2CxUilNg8o73EOuP92AEHgVLb0XGvi24T4vHhidDFsidjCiJojCHYKBqCKWxX2xezjZuZNhtUYxqYemzBbzAiCQCOfRoytM5YQpxAcNA7Wrut41eH0iyVB+V1RtapuVXG1cX3oPTEmJpKxYgUAHq+9RuY331B0/TpF16WUev/PPiP1i7kYY2IxxMZReOUKAKbEJEyJSdJl9+lj7c+mciW8355O8jszSP1iHtoqVcFsIuenbSAIeL81DUEQULm54fbiQDKWS+d27NAeh5Yt73/rlEoc27R56HXIyMg8feTAW0bmKfHe9musOx2LRqVAq5TeXEa1CsXdoSSde2TLciw+HInZIuLtZCNVtG15FbJj4LnV4FBS+83edyDpEti6QaePIP6sJIhm7wVnl0sr3yCplXf86KmLo22P3M62yG0sar+ojPXW/tj9PBPwDCqFCotooXO5zsTnxVt9tau7V2dB2wVW7+ttvbeRVZQlB90yMjIyT4kio5m4rALr9xtJOt7ufk9KcUYEFEglUNzaRR2VLwAnzdXwc7dFsHFGOXQbmArBNeSB59EZdCy7tIwKrhXoFtrN+rzI0edwLUNa2W3s2xgnjROzzswiLjeOsJQwGvpIqetV3KTsrZuZN6193n1WKAQFo2uP/s1rbRfUjj3P7iGzKPM322Zt3AgWC3ZNmuD52lgUtrakfvYZAK4vvohT505kb9qEMSYWY1wshqg7pY7XlC+PTY0apba59OtHwdmz6LZtJ37sWBQO0iSB25Ah2NaqZW3nNmwYWRs3gdGI99SpvzlWGRmZvxdy4C0j8xS4kaRj3elYQFr5NpgsVPV1YlzbiqXaudhpWDm0AWejM6V9dw7DpWJblHX94KUdxWrj2+BMsUp5n6VQqaNkAXaXxq9A7EmprVso2DxdL+lN4Zt4/+T7TGowCQWlhdlWXFnB/PPz6VW+Fx80/wC1Us3wGsMZXHUwOoMOrVKLvdq+lEiLg8ah1GqFjIyMjMyfS2RaHqJY8v1EZAakR8CpRdBqovSMuYshD2+DlKp90lKN8i620vagh6dti6LI9KPTORR/CIAll5awvMNyAp0COZ9y3rqq7WPvA0h12ptvb2b4nuHMazOPdkHtqOpWlUqulajuXh2TxYRK8ftebf0c/PBz8Hv4eA0GsjdJYm2uL0je466DB5F/4gSCSoXXlMkAqIMC4TgY4uIxREUB4DnhTQovXcL1+RfKiJAJgoDfhx9iyc0j7+BBzHo96uAgPMe/Uaqdys2N0K1bJM9s/9/Qd5GRkfnbIQfeMjJ/AgUGE7ZqpfXhWsXHkc+fq82NJB3PVPJk/40URrQIRaMqqxbeurIXrStLK70cnVOyI+kifNsPWk6QBGkAmr0uBd2/RqmGcq2e8FXBmaQzeNt7W1+Gfk1kdiRfX/2anyJ/koacn2T1wzZajETnRLPwwkIAQpxDSh2rVqpxty2t6CojI1ospH+1iKKbN/H76EOULi5l2uQdOYLCwQG7evWe/gBlZP6B/Hw5iexCA/WDXani43TfNhGpeQCU87DnTno+15N0xG5dSlD8dtDnSs8ZAJWttKoNxOFDIh60dLZ9pHGsub6GQ/GHUCvUOGocSchLQESK9pv5N+Ny+mWrTSRA34p92Xx7MwAXUi7QLqgdnnaebO65mSJTEc2+b0aocygrOq54IpO1pvR0CsLO4/BMKxQ2NuT+8gvmjAxUXl44tpVSuRUaDUErV5Q6ThMYBIAhNgZ9dDQAju3a4/Hyyw88l6DREDB/HolTp5F/8iR+n8xCYVv2Pqp9ff/wdcnIyPw1yIG3jMwT5kREOi+vOUcVXydWDm2Ai50GQRDoV7+klq1VJc+yB55aAufXgIMXeFQCr6pw54iUKt7va9j6KsSdgu+ek9oHNHyqomhX068yYu8IXLQubOm1BQ9bj1L7D8QeYPzB8daXpmHVh/G/+v9DEAROJZ3ig5MfYBEtmEUz7YLaMbLmyKc2dpl/JqIokvzBB2R/Lwk0pfn64vP2dLI3b8aSl4frkCEUXb9O3KhXQKnE/4u5OHW8z0QUYDEYyD9xAvvGje/7MmtMSaXwwnnsW7RE6WB/nx5kZP49fHngNjeTc3G1U3N2entUyrKTwHcD7yahblYbS1PsWVAA17dKZU4AzV+Hw7MBSHJtAEng5/LbgXeMLoZ5YfMAmNJwCj3K9+BA3AFriZFWqaWZnxRI36WmR4lCd03P0mrdt7JuUWgqJCk/qZSbxe/FmJJKzMCBGBMSUHp64NC8BXmHDgHg8txz9/XovosmSLL3LDwXhlhQACoVmsCH1LMXI2g0+M+dg2g2Iyjl0ioZmX8bcuAtI/MEicssYOx358k3mAmLyaLO+/vYMa4FNfwfkOptsUge2eF7YHex4njqNYg6WNKm9vNQrSd4VYODH8K1LWDrKomlKR/84H/S7IneA0C2PpsPT33IF62/sK7omy1mvgj7AhGRZn7NGFNnDLU9S9RUj8YfJTZXSrW3UdowueHkpzZumSeLKIqIej0KG0mhuOhWOObMDOybNn3gMYb4eBInTUZbuRJeEydiiIwk+4cfcO7TF7t6de97jDk3l+QPPkC3rcQvNmv9ehS2tmQslyx2VH5+5O7aVXyAmYQJEzH+739oQoIpOHOWvMOHcWjVCo9x40gYP578Y8ewb9aUwJUrS6V6GuLjiXlxEKaUFBQODrg8+yyugwejCZBTOWX+nTQr78Gd9HyyCoxEpuVT2cexdAOLBeeIn/DEjwpe1WgU4sat6FhCFcnSfrMB8pIBAZq8Che/g5w4zgpSMOzv+tuB9/7Y/ZhEE418GtG/cn8EQaB7aPdSbe7Wcd9FEAR+7vMzYSlhdAwuPcl2JU0SMavmXu0P+0mbs7OJGzlS8sIWBMxp6eRs3QqAOjAQ1xeef+jx6uIVb1NaGgCawMCHBuq/Rg66ZWT+nciBt4zMY5CepyevyESIR9nZ9CKjmVfWhpFVYKSqrxPpeXrScvWMWXee3eNbYqf51T+3Ewth73QIagqpkhoqtQdCSAuIPiatKAhKaPGmtM+jAjz3DbSbCWo7yY/7T2Z3tORH2jmkM2/UewNXG1e+CPuC/bH7+fra1/St0BcXGxf2x+4nWheNo8aROc/MKZPiN7bOWH6J+YXE/ERG1hz5m3V0Mk8PQ0wMKZ9+hn3jRrgOHFjGmuZeTFlZxL82Dn14OEGrVqHy8iL6+ecRCwvxHP8GHqPLihiJZjOJk6dQeOEChRcukLtnL+asLAB0O3cR/O1abKpWLXVM4cWLxL8xHlNKCggCvh9+SO7eveQdPmwNugFSPvzI+mJr16gRBWfOkPrpp6X6yrxzh+wff8SSKwn35Z84Sc7mzbj062e9/tjhI6RzqdVY8vLIXL0amxrV5cBb5l/LjB7VuJaYw+k7mVyOzy4beF/6jpGpH1HTvh5G7870ruPHvu234NavOvKuIU0E910Odw5zPuoZNMpsfJ1/ZR12H7ztvKnnVY+OwR0fK1AOcgoiyCmo1LYTCSeYfVZada/uXv2R+3oQaV8tQn/7NiovL4JWf0NhWBj62xHYN2+GfZMmCJqH21n+enVbExr6gJYyMjL/JeTAW0bmETFbRPotPkFcViGrXmrIM79KFw+LyeJ2ai4eDpJAmsFkYfbum3So5l026M5JgAMfSp/vCtQENIIe80GlgbovQrfPwWwEW5fSx7qV+3Mu8FdEZUcx6fAkAAQEOoV0YniN4ehNehZdWsQXYV8w//x8pjeeTvfQ7kxsMBGloLxvXZ2d2o4lHZZwLuUcfSr0KbNf5q/Bkp9P3JixGCIjydu/n5yftuEz451S3q+iyUThlSuYs7JInTsXQ0QkAEkzZmBTqRJioVR/mTZvPiiVZWoYM1atovD8eRT29ihdXKQVJEDl54spMYnYUaOwa9AAc3Y23pMmoa1UiYQpUzClpKAODsLvo4+wa9AA2zq1yTt2DMxmnLp3p+DMGSlYBuxbtCBw8SIyVqyg8NJljKkpaIKDsa1dm/RFi7HodAgaDY6dO6Hbtp2UWbMxpqZiiIhEt2cPmM2og4MIXrMG/a1b5Gz9CadOnZ7GX4GMzFMjOj2ftadi6FvPn+p+ztQKcOb0nUyuJOTwXIPA0o1vSZkkjcXLEKAEWy3P+6VJgXeV7hBzHAqzIKiJ1D64KQQ3pZN9HM4OtjQq52btKiwljCPxR2gf1L5Ueni30G50C+32RK7N39EfrUHEsRCquFZ+7OONKSnoft6Jc4/uqDw90d+WhOI8x49HW64c2nKP99xV2Nmh9PTAnJYOgDb06Ty3ZWRk/t7IgbeMzEOISM3lfGw2/eoFcCNJR3SGZK0y5tswNrzSFJNFRK0UqOLjRPMKHmwf14K8tAT81rWGoKYsfuFzSRRt1cvgWxsaj5JUxg98KInRBDSEar0h4za0niYF3XfR/LV1ppvCN1k/v3P8Hco7l6eCawVG1hqJUqFkd/RuorKjaOrXFDu1HUOrD31of+Wcy1HOWX75eJqYsrKw5Odb1W+NCQnkHztO/ulTKB0cMCYkYoiMROnujmg0UnTtGtEDnsexQwc8/zcepasr8a+OofDiRWufKm9vLIWF6G/cQH/jBiB50uZs2ULanLnYN2mCbc2aWIqKyFi+gvSlkvq+91tv4dipIzk/bsG2bl00wUHEDBqMPjyc3F1SZkXCpMm4D3sJY0wsSldXym3ejLLYVkdbvjy+H32IIeoOnq+NRbd7N4mTpfIM95EjEdRqPF59tcw9cGzXjsyvv8axc2fs6tfHEB1D0eXLpC/40trGvlkzfD/8ALW3N2pvbxxaPXlhQhmZv5ofz8ez8tgdItPy+GZYI2oGuABwKT6ndEOTAaIOS58tJqkUqvbzkBAmbQtpAcHNYf/7UGtAqUP7Nwykf8PSQfyuO7vYcGsDeYa8MnXZT4oAhwA+WmMmKA2EH94ntc81vP43/pGOzTtyhMTJUzBnZ2NKS8N7ymRrJo3a7/cLmWkCAiksDrw15eQVbxkZGTnwlpF5IIUGM4NXniEpp4gio5khTUM4OrkNLT89SL7BTPcvj1nbLhlUn841fCR12JuLIO2m9CfrDsSdBWM+xJ6A00vAoyKkS7PpdJ4FAQ3+oiuE5ZeXczThKNMbT6eyW8kqQaGp0KpM7u/gT0JeAh+c+oAVHVegVqoZVWsUo2qNIr0wvYzImsyTJ2f7dpLemo5dw4a4DnwBh3btHpqaacrKImPJEjK/+x6MRpQeHmAyYc7OLttYrSZg4ZdoAgJInfsFOT/9RO6+feTu34/K3R1TWhoKOzs05cujDvDHe+JE8o4dJ3nmTACcunbB75OPEc0maTV59mz8Zs0ibuTLGIrVfJ179cS5bx8EQcBtyGDrqYNWriBz7bcoXVzIWLkSQ2QkSe++B4Db8GHWoPsuLr17Wz87de9O4dWrCCo1do0bPfBeaAID8ZlRIkLoP3cOGctXgMWCwtER5+7dsKlW7YHHy8j8W/jpUiIAfepKE3G1irVHbiTpMJgsaFQK4rMK+G7D90w25JYceGO7FGDfDbz960NgI6m2u/h3aNHFRRyKO8TSDktxtXG1HiqKIkfjjwJwPeM6Q3cNZUWnFURlR+Fj74Oz9slYXVrS0glKKz5najoZS5fi3KM72goVAEkzIuXDj1C6uODS/zlEo5H84yfQ7d1D0aXL1n4McZIWiSk1FQCV532EUB8RTVAghRcuSJ/LhfzufmRkZP49yIG3zH+e9Dw97vaaMoHMymNRJOUUAbBg/2361gsg0M2Os9Pb8+ziE8RmFuDpqKXIYKZ+cPGLhijCpe9LOok6JP03pCWotBDxC6SHS9tqPPvEg26LaOGXmF+o513vNwPi1ddWs+DCAgCG7xnOW43fIik/iZE1RxKWEkauIRd/B3++7fotrx94HW97b9S/EnOTg+4/H1EUyVi2THpRPHGC/BMn8Jo8GffhwwDQR0WRs2UrZp0Or0kTEY1Gop/thzFReslGqcScLq26oFJhW6MG9q1aIhbp0UdE4NyjO3Z1JYEzv08+xn34MFLnzSdv/35MaWmofH0JWr7M+gIL4PJcP3L37aPo+nU835wAgNf//kfunr0UngvjTt9nseh0qLy88H5rGo6dOt13okDl6YnXm/+TPnt5kThxIphMKJ2dcX1hYJn29yIoFPi89dZj309NQAC+77372MfJyPyTySk0ElOcsdW2iqQaHuxux9CmwVTxdcIiioSn5DJk5RmGFhwEFSRqy+Onj4SI/ZARAfmpksuGT/GqdfG/6V13drH40mIAjsQfoVeFXtbzRmZHkpifiEpQEZkTSaGpkLPJZ/ns7GdEZkeyuP1imvs3/8PXp78lFZ9rQkJQ+/mRf+IEur178axQAVEUSZw6jbz9+wHIXL269MGCgG2dOhReuIApOQVLYaFVE0Ll5fW7x3RXYA147FR1GRmZfydy4C3zn8NiEVEopBeGzWHxTNh0iUmdKjO2TQUKDCZrPfaNZOnBa6dRkp1XwOoDlxnbpR6ejlr2/q8VRUYzLnYaLEY9il9mgoMnBDaBrGjQOEKvL2HnJKjQoaR2Oy8VEi9AdmyZFL0nwY+3f+S9k+9R27M2a7usfeCqaHphOntj9gLgY+9Dcn4yU49OBaBdUDta+LdgZ5+dJOUn4WHrwXfdvnviY/2vYMrMxFJQ+LuFuvQ3bqC/HYGg0eDctw/Z6zeQtmABju3akr5oMTk//WRta4iORtBoMCYmovb3x+e997Br2ICia9cR1Cq0lSqh0Gofej5txYoEfrWQgvMXyD92DJcBA1B7l375FBQKApctBVG0qu+qfX1xG/YSGUuWYtHp0JQrR9A335Q59kE4detKzrafyD9yFLcRI2RLLxmZJ0hUmmQN5u2kxdFGmkAVBIH3GpnhyjLiz9ak/y+uZBcY6Wh3FSzg1nESHPkYcmKlZxlI7hrqEsXyGF0M75541/o9PCu81HmPJBwBoIlfE3ztfdkUvolPTn9CtC4aAeGJCKEBFN2UAm+batWwb96c/BMnyN27D88xY8hcuZK8/fsR1GrsmjQh/9gxFLa22Narh0Pr1jh27IApLU2asExJtqaZC7a2KBx+vxf4XUsxpbs7SheXP3yNMjIy/3zkwFvmP8WV+ByeX3aSDtW8+aRvLRqGSAIwn+25RfMKHoxeG0b/BgG81rYiXw2sx7DGyWgvr8Xr4kLsTxUR7vcjlWo3xyZsGTaFWdByIoqjn8NpabYfu+IV4Oq9oHofqX773uDXwQsq/XmiTUsvS/W0l9IukVqQird9ifJ5gbGAHH0Ovg6+uNu4k6PP4cWqL/J63dd589CbXEi9QJugNtb2gU6BBDoFljnHfx3RYkEfEYG2YsXfVOLVR90h5sUXsRQWErp9G5pA6X6asrLI+v57DBERmDKzUPv74dDqGRxatkBhZ1eqj7uBtUPbtvjMnIkhMoqCs2e506cvloICUCqxb9GcwrPnKDh9GgBBqyVg0VfYVJbKBx5k2fUw7OrVfehxgqKs76/7yJfJP3YcQaMhYP68x0rTFASBgPnzKTgXhn3zZo89XhkZmQcTlSb5cId6FAeSFjN811/KwgJcsSGvaBlt/ATKZ94BBGyqdIDUS9Lz7a7FZUBpe6855+ZQYCrATmVHgamA21m3S+0/Ei8F3i39WxLqEsqm8E1E66IBaBXQChcblydyfXdXvLWVK+PQtg0olehv3iT7hx9InfsFAN7Tp+P6/ADMefkotJpS9l53JxDN6RnWbCGVp+cfsiWza9AAhb09jm3b/HZjGRmZ/wRy4C3zn2LlsSjyDWa2XkxEpVTwWb9atKjgwbGIdPovPYnBZGH3tWTGtq0AFgsNTo6VXkyKn70hx6eB+WXYLa0OE3UI4s+VnKCgOKW3dnGa7B/0En0crmdcJzlf8ljd129fqaAbpHTA906+R79K/ZjWeBpTG02lmV8zFIKCxe0XFw/36Y33n0rmmjWkzpqN+6hR1jTp+2FMSSF25AirdVbGsuX4vP8ematWkb54CZa8vFLtczb/iNLVFbdhw3B7cSAKe3tEk4mcn3cC4NyzJ4Ig4PPuTKJ698FSUCAF2F8uwKFVK/JPniRu1CuIRiM+77xtDbqfJkoHe0I2bfzd/x8pbG1xaNniCY9KRubfT77exP6bqRy6lcr/2lci0K30BF5UuvR7E+pZnEmSdAkifkEUlJhRYC8W0c0jlU+aaGEn4FcX7D2g2TjITQRBAY5+0Oy1Uv2+Wf9Nmvo1xUHtwAenPihVjnQo7hBhKVJdeKuAVvg5+DGixgjyjHm0D25PA+9HL7USTSbyDh9GHxWF25AhZTJ3im7dBMCmSmVUrq7YNWpIwclTJL39DgDOvXrhMqA/wH2zaZRubghqtVVkEkDl9fvruwHUfn5UOnniN63HZGRk/jvIgbfMfwZdkZFdV6XAtJyHPf/rUAlBEJjZoxqd5x/FYLKgUgjMea4OWpUSzq6Ugm6VLebW01Ac/RxN6iXYVvziISggTlphpHpfKNcKdowHj0qSN/cT5lLaJZLzk6ntWRsfex+MFiNJeUlWP9PN4ZsB6BLSBR97H+sxPnY+eNt7szViKyIiAY4BqBVqWviXBDhywP3o6Hb8DEDG11/j0rcPhoQEdD/vxGviBFRuUgZF3uHDJL37HqakJFReXphSU8neuhWFnZ21vlBbpQrOvXqhdHVBf+MGuft+wZiYSNrcuWRv3Ijv++9RePky5vR0lK6u1oBUW748PtOnk7VxA95TpmJfLCxm37QpIRvWY0xOwaFN66d+X+4i/78kI/N0eGP9BfRGC5kFBm4m6dAVmQDJ2vKH0c3wdCwJTu86cpTzKA46488CYAxpzdGoHNoJ5/iobg52MVel/RXaS/919of+ax44hhDnEEKcQ7CIFrqW64pSobTuc9G64GHrQcfgjgQ4Sr7W4+uPf+zrzD91msS3pmFKTAJANBjwHDvWut+i12O4Ew1Iv6sATh07UnDylLStUiV83p350N8mQRBQ+fhgjIuj8PIV4I8Jq1n7lYNuGRmZe5ADb5n/DNsvJaI3Wajk7cDuN1pZ67wrejvycstQlhyO5M22IdQ0X4dwHewrVkJuPxNlk1fBxhF2FK9wlnsG2kyH9QNB6wDd5oCdmySWZu8F90nD/SOcTDzJq7+8ilk0A+Br74ubjRuxuljmt51PdffqbAzfCMCzlZ4FJFGusfvHkqPPoY5nHS6mXUQhKOge2v2Jju2fjqWwkOT33kew0eL52muoPB4sGGdKS6PoavGLqdFI3GuvSS98ZjNKR0e8p00l9Yt5ZBRbaKkDAwn6+muSpk+n4PRpa9DtOeFN3EeMKEnX7t0br0mT0P38M6nz52OMjyd2+AjreV2e7VsqLdL1+QG4Pl9WI8CmWjVZoVtG5j+A0Wzh58tJmCyidZsggJudhpiMAoasOsMPo5tir5Ve8+YPqMPzzTSk62+RVuCIZ9wZADQhTWgWYkPm4fMUJhxmee4tdO6uzLwbeD8iCkFhzQy7Sx2vOmzsvvEPpZMbU1NJGD8ec3Y2go0NYlER2Rs24jFqFPknT1Jw4YJk/2c2o3R2toqhOXboQMpnnyMolQQsmI/C1vY3zgRqb28p8L4iqZyr/4CwmoyMjMz9kANvmf8Mm87FA/Bc/UBr0H2XKZ0r80LDAIL3vwrHtpXsCGoKjV6RPtd7qUTdte8ycPSBN2+AaC4Rm/F5Mh6lUdlRfHXxK2Y2m4mt0pYZJ2ZgFs1423mTVphGUn4SSflJKAQFBcYCq1BaoGMgDX2kGrw8Yx4eNh7kGnK5mHYRgOZ+zfGyk18m7iJaLCROnUbunj0A6H7eiVPXrqjc3TDEx6O/HYFzr564v/QSAHlHJQs5tZ8fxtRUDBGR1r6yt27Fpd+zZKxYAYDbSy/h+fo4FHZ2eLz6KrHF9deuA1+QfKd/tfoiqFQ49+qFQ7t2pHz0MTlbtqAODMRt0Iu4Dny4wreMjMx/i5iMfEwWEXuNkuVDGrDq+B2GNS+Hv4st/Zac4EaSjv03U+lZ2w8AlVLBsuuzuZh2kc80n9E5Xgq8CWiIra0Lsy+7sFmMBgctClFDL40S55w7BDoGolLc/1Vx0cVFeNh60CmkUylbMFEUrb9vnnaPt2psTElF5SXVVosWC0lvTcecnY22alWCV39DZOcumFJTSV++nIwlSxENBnL37gOk1e6751V5eBC65UcEjQa176N5cat8pEyxuyvrT2LFW0ZGRuZe5MBb5j9Bep6ea4k5KBUCveuWVZcWBIHg2B/hxjbJLsW9Ati4QO9FJavXCgU8v670gaonn0aWo89h3IFxxObGYquy5cMWH/JVu69YcWUFHzb/EJPFxOX0y1xJu0JV96q08G+BwWwgz5BHA58G0soD4KhxZGvvrSTnJ/Pt9W85m3KWMXXGPPHx/tMQTSZSZs1GHx6OoFKRf+IEqNVoQ0PR37pF9oYNpdqn3riB0tkFlz69yTt8GADn3r1BEEhftAi3IYOtaeJxr4wGsxn7li3xnjrF2odd40a4jxqFpbAQ7ymTH5ryqHRwwO+Tj/F6839S3aFS+cC2MjIy/00iUqWa7QpeDjSr4EGzCiWZOksH18dZq6CCj4t1283Mm9YJ2Lj0G5KzBoLkya2xJ/aemmmLIDB4j2RXOKDyAN5u8naZ8xcYC1h+ZTkmi4kmvk1w1jqzO3o3Sy8tJSI7gokNJtKjfA/cbNzuO35TZiYKG5tSYpJ5R44QN+oV7Fu2JODLBaQvWUL+sWMIWi3+n32K0skJl379yFi2jPQFX1qPM0RKE6DaypVKnUMTHPwId7IEtU9pXZQ/YiUmIyMjcz/kwFvmX4OuyIjJLOJmr+F8bBYf7riOl6MNb3WtSpC7HauHNWLn1aSSure0cDg6B27uALdQyChevWz7DrQY/5dcgyiKTD82ndjcWPzs/XizwZsAVHKtxKetPgVAo9TQxLcJTXybWI/TKDUMqjbovn362PswseHEP3/wTxFRFNHfuIGmfPlSIjv6iAiyt2zBffhwVO7u5J88Sd6xY3i88gpKJydEk4nEyZPR7dxVqj/fD97HuUcPcvfsQR8RgSkzU6rNTk4he+NGkmbMQFApyT9+HACHZ1phW7s2bi8NRenoiNLVjbR586xquB6vvlqqf0EQHirEdj/k1RYZGZkHcTtFCrzLe/3K7spiof75t+DGDujwLjQYwfm4bD4+utPaJD5NSqXGqxrYOAEQo7UFTLyblsHn3r7kW4yIiGyP3M6EBhOwVZVO1b6QegGTxYSvvS+BjiXuFxHZEQB8v/szalQDty5DrftEiwVTSgppCxeS8+MW7Bo3Jvibr637dXulzK38o0eJ7NIVU5K08uw9bSraChUAcB3QX8oqslhQurqirVyZglNSLbdN5Sq/51ZaUXn7lP4u/wbLyMg8YeTAW+Zfwe2UXJ5fdoqMfANVfBwJT8nlbulbqKc9kztXkVYFPPXwTXdIuQaFmSUdJBe/iIS0lFRc/yIOxB3gcPxh1Ao1C9oueOBqwX+dtC/mkbFsGa5DBuPz1luAFIwnTJqM/sYNiq5cxXv6dOJGv4qo16O/cRP/eV+Q9M4MKa1crcbz9XGIhUVoQkNx7t4NAKeuXUudR7RYMOfqyN21m8RJkwFQurpiU6OG9NnREQCXfs+StnAhmEzYNW78u+y7ZGRk/tuk5hYRk1FANV8na232fbFYyI+9hAItFb0cS+/7ZSZc+l76/PMEiiKOcjngbS4nR6MtXhSPy4kG4LxPBewybxLkGEQqkjBb+4JCevX4CcHJl64/diUxP5Gj8UfpGNKx1GlOJ0ulM418GlkzeCq5lKw4T/8B7HI+xVivCyovT+JHv0resWNgNlvbFJw6hTElFbW3tLJccOasdZ8pKQkEAe933sb1+eet29X+/jh174Zu5y58P/oIm6pViOrWHUtBATY1a/zGHX44KnnFW0ZG5k9GDrxl/hV8vPMGGfkGAG4m5+JAAXudPyJNE0jFNptLGh78CKKPFn8RoHIXaDoWdEmQcRsavgyKvya1t8hUxKdnpFXtl6q/RGW3p28H9VcjGo0kTJiIwtEB3w8/RBAEjElJCGq1VfRMt3cvGcuWAZD7yy94T5uGIAjk7d+P/sYNAArOnCHmhRcQ9XoA8k+cIKJ1G8n3WqUiYN4XOLZr95vjERQK/GbPJj0khMzVaxALCnBo17ZM+rfKwwPX/s+R/cNmPMe/8SRviYyMzH+EX66n8taWK7Ss6MHaEY1L7csuMOBsq5aC3Ks/MDXmZbTKZ6ng1aik0YV1cGKB9Ln2C5gvbcTm1lbuRIWicMi2NovTZ6EXYJYxkRvbn+Pbrt8C4GI24xzQBFwkBfJeFXpxO+v2fXVBTidJgXdj35Jx3nXYUFhEPLKkANuULK1a3y3TAbCtXRtzdjaGmBjyT5zApU9vjElJGGNjQaEgcPEiMtesxaV/f5w6lQ74Afw++gjvqVOtLhJBa9ZgTEjAplKlMm0fB7VP6VpwecVbRkbmSSMH3jL/SHKLjMRmFlDdTxJ0mTegLh/8fJ1XWoVyPUlHrfSd+B27g5/+DlxZBw2GQeYduLRe6uD57yH0GdCU9fN80hgtRo7EHaGyW2WrpUqhqZBbmbdIKUihTWAbNEoNyy4vIzE/ER97H0bWHPmnj+uvwpSZiSk52aq+nXf0GObMDJx69iT3wEFyi9MNHdu1Q+niQszQl8BoRFOhPEoHR4pu3izpK1F6WVMHBZH21SIAbGrXoujSZSwFBSjd3fF8/XWS330XS0EBKm9v/OfOwa5+/Ucer0KjweuNN3AbNIj848dxaNPmvu28334br0mTHkk9V0ZGRubXXE3MAaCytyPR6fmEFFt/XYnPoddXx3ixcTAf9K6BGH8OAeimPIX6bqq5sRB+eVf6/MxUaDONC9l2NIhZSeui/Wx0dbGeJ6V4dTuz+L8/3v4RgCD3qtBhubXdgzRBcvQ53MiQJjnvBt55x4+Tf+QIXRt24NqdM0AGAOacHITi30SlszMVjh5BodGQOm8eGUuWkn/8OC59elNwVlrttqleHYdnnsHhmWceeJ8EtdoadAPY1qiObY3qD2z/qNxb4y3Y2KBwdHxIaxkZGZnHRw68Zf5xbDoXx4c/38DZVs2BCc+gUipwtlPz+XO1AckejO8PlhywbyZU7irVc4tmKN8OqnR9QO9PHgUKvr3xLWEpYdTzrofOoCMyOxKLaAGguX9zPmv1GftiJGXWyQ0nY6e2e1iX/2jix71OYVgYAYu+QluhAnFjxoDRiMLJiazvv7e2S5s3H9FiBqMRoJSCuF2TJoh6PYUXLpB/8hTK8HD0N26gsLcnaOlSUufNI+fHLfjNmoVDyxYonRwpvHgR99GjUbm6/q5xq9zdce7Z84H7BYXC+oIpIyMj87hcTZAC7xXH7rDrajLHprRBEATm7w/HIsLaUzF80LsGQl4KAJUUCVhU2YA9XPgW8lPBORBaSZoe9XuMgYUraam4gkpdEpiKgkCCR3laBLRmc8Rma+Ad4l5FssX8Dc4mn0VEpJxzOetqeOqs2ehv3+btJosRWowgbk4/AMw5OquAmtLFBUWxr7VD8+ZS4H3iBKLFQv4ZSWXdrlHDP3gXfz9Kd3dQqcBkQuXl9VARTBkZGZnfgxx4y/yjOHQrlcmbLyOK4O6gISmniEC3XwWphnyI3C99dg6EnDj4sp60HaD11D99nAdiD+Dv4E9lt8ooFUpervUy5/adIywlzNrGw9aDPINk+WWnsmNdt3XsvrObDsEd/vTx/VUYk5IoDJPuQcrHn6CtXNkaWCfPfBdTaiooFChsbNCHhwOgdHMjeN23GO7cQTSbUbm4YFuvHulLlhQH3icxxMQA4DpkMEoXF3xmzsTnrbcQil/ynLp0walLl7/gimVkZGR+G6PZws2kXABs0DO34H2ydnbFrdtMVHedNSi26ioOvAEU0UegZj84Xpxi3vwNUKoBEDwqIPo3QEg4h0KtwwKMydVToUCH8MwMWvpUZnOEVIr1XKXnqOdd775ji9XFciH1Ar0q9JK+58aiEBQ09pFWu0WTCX10NACmlBQ0NiWCl2ZdDgo7aUJS4VxiOWZbuzaCnR3mzEz0t25Z67vtG92TOv+UERQK1F5eGBMT5TRzGRmZPwU58Jb5xxCdns/r319AFKF/gwA+6VsLpeI+M9IR+8FUBC7BMGAtrOwIBkkBloqdIPDPebDf9S7dE72HiYcn0r9Sf95p+g4AzfyasbPvTo4lHMPHzofqHtXxsvMiIiuCUJdQFIICJ40T/Sv3/1PG9nch79Ah62djfDzG+HgQBJROTlLQDTi0bo1N9Wqkf7kQAO9p09CWK4e2XLlSfdk3bUr6lwvJ3bcPLBYU9va4D5UUdAVBAM2Tt3qTkZGR+TO4nZKHwWzB0UbFC+7RNM64SdHFFOg2E6UA1YVo7og+ZOQb8MhNLjkw6hBYTJATC/ZeULe0u4VQawBpyRewCCIqBEalp2BwL0+zW8vRRpYEyMOqDyPQKZBfk5yfTLct3VAICpr6NcX29DVazdpAz/cWItaSVMSN8fHWCVRTejoKhxKldYtOh9m2eMX7nsBb0Giwb9SIvEOHyFix0lrfbfsYZUB/BiofHynw9pIDbxkZmSeP4rebyMj8eZyKyiCrWBTtXvL0plLfz0Vn0n/pSXRFJuoGufBB7xooMyMg7kxJo8gDkrjM3Truqj3Atza8eQPGnIbRx6VA/E8grSCNl/e+zPGE48w5NweAInNRqTaBjoG8UOUF2gS1sabnVXCtYPXd/rchiiKmrKxS23IPSCUAtrVrW7c59+6N1+TJ1u+uLzyP29Ch2DVqhMtzz+FUrDj+a2xr1pRSGC1Syv7d1W4ZGRmZfxp308yr+znR0kOaKLYxZoHJwPym+fysfYsP1auIy8jHkJNUcmDEPtgnTfDS7DVQ/6rcpcazeIsCB2Pi+T4hESVwrd7zmEQT9ip7GvpIqd1HE45yP3zsfajrVReLaOHnqJ/R7dqFMSYWw7bdeNpJwak+Ksra3pSehjmzxDHEnJ2DOUe6tnsDbwD75s0B0P38s3S91aujdPiVPdpT5m6dt7ziLSMj82cgr3jL/GXsvJLEmHXnyyi4br+UyPs7rnN8Sls0KgVFRjNTNl8mNVdPRS8HlvUJRLvjdbi4DhCh92Kwc4fvBkjf71K1h/RfO7dHqlv7LTKLMpl2dBoxuhj6VOhDoamQcXXHoVQoWXFlBaeTT3M25SwW0YKvvS/vNHnnD5/zn4g5L5/C82GkzV9A0bVruLzwPD7TpiEajVa/Vd+PPiR13jyKrl/H843XUXl5URAWBmYT9s2bIygUBK9Z/dDzCGo1tg0bkH/4CAoHB+tqt4yMjMw/jbvCajX9namizyjZkZeMKvUKAJ1dEzG5gMYiTeqaFWqUBcVtfetA41fLdmzvjtDhfTyubsYDyPKoyBfZFwGo7VWbiq4VOZt8ll9if2Fg1YH3HVuv8r24kHqBnyJ+olWyFBjfLRkCMNwbeKell5oANet0CLY2ACidnEr169S1C9mbNiGaTGgrVMB9+LCH3aKngn2zZuj27P1LU95lZGT+vciBt8xfxvxfbgNw9HY6OYVGnG3VGM0WZu26SVqunr3Xk+ley4/YzAKi0vPpXsuX2X1rYP91W0i5UtLRttdBZQOIxTXd8eBVFQKenEhLRFYErx14jYS8BAAWXpTSoG1UNoyuPZo36r3BmeQzRGRHAPBmgzexUdk8sfP/nSm8fJm0hQsxxsZhSk2VLLvuIfv79ejDb2PXsAGi0Yg6OAhN+fIELlxYqp3fxx899rmde/Yk//ARPF59VV7tlpGR+cfyQqMgQj3sqRngguuJBOv2vLR4HHKk73b5CYiGNAByRVsUfg2xjz8CKlvouxxUDyivaTpW+gPcSDzB5X2vAFDHsw6NfRuz6OIiIrMl8UpTRgaJkybh1K07Ls/2BaBjSEdmnZlFZE4kdyLN+AOGmBhMaWmoPD3RR92xnsqUnobK3d363azTobgbeLuUXvFWubsTuu2n33vL/hRc+vXDqUcPFFrtbzeWkZGReUzkwFvmLyEpp5DwVElI5pc3n8HZVhKD+eliIgnZhXg4aGlfVUr5CnC1Zce4FlTzdUK4tkUKurXOMOgHOLkQrv8EBiMENoGh28FUKAXiT8iP+3bWbYbvGU62PpsAhwCGVB/CjqgdKFDQpZwk2GWntmNu67mM2DOC6u7V6RTc6Ymc+++GpaiI3P37yd2zF0GrRWFnR/YPP4DZXKqdwtkZlz59sKleneR336UwLMy6QuLYpu0TU4t17tYNhxYtyqQwysjIyPyTqOrrRFXf4hXhXTHW7fFxURRdu0YdALOenKhzuABpuBDYeDikXoCun4FniYd1cn4y19Kv0TZI+q3dHL6ZqJwoOgR3INChpI67lmctKrtV5vtu3+OidQEge9Mm8k+cxJiSag28HTWOtAtux89RP+OaV5JVVnD+Ak6dOmKILHGcMKelY/IsWbE352Rbg1jFr1a8/67IQbeMjMyfhRx4y/wlbLmQgChCoxA3KhT7kFosIksOSw/wkS3LYaOWAmc7jUry67ZY4PBsqYOmYySRNO8lYNJDbjL0XyPN+D9o1v93cjX9Ktn6bGq412BR+0W42rjyQpUXyrQr51yO/c/t/1dYkKTOm4c5Oxufd95BUEp/D4b4eGJeHIQpJaVMe6euXXEZMAC1txcqT08U9iX+6LY1a5Dx9Tfk7t+PRafDuU/vJzpWOeiWkZH51yCKkBlt/arIT8aSE29V5Llyah8tAZ3SHXXNPlCjN/zqmfPhqQ85HH+Y+W3m0zaoLQfiDnAk/gjNv7mIS56K0F4hWATw33MFnUcSNe5xfNDt3QuAMTYW0Wy2/v7/r97/cDHbYKffYG1beD4Mx44d0N+5Z8U7LQ1VRokftiVHh1lbvOLt7PIEbpCMjIzMPxc58JZ56oiiyOaweACere8PSMIyM7ddIyI1DycbFS82DoKky3B2ObSaDC6BcH0rpN0EG2doPFrqTGMHAzc84EyPPp4Ntzaw684uQl1CqedVjyJzER2DO+KsdaZPxT44aZxo4NMAZ+3Dg7y/W9BtiIsjbf4CPEa/grZChUc6puDsWTKWLAUkATOXZ59FNJlInDwFU0oKKi8vnPv2QWFrhzE+DvsWLXHq1PGB/WlCQvB97118Zs4Ak8lq8SUjIyMjAzEZ+YTFZFHJ25EaLiYw5Fr32RSm4iiUiJW5ZZwHBRhsi8W/BIHdd3YzJ2wOc5+ZS03PmqgU0qtdSoE0SZqUn4TaJOK6/zyFwLqZ28DOlrjWHRA0GhzbtUPQaDDEx6O/fgMA0WjEmJQMZhPJ772H+8sv82bIS0RS8rwtOBeGOSMDi04nBf+iiGg0Yii2FoPiGm+bu4G3PEkqIyPz30YOvGWeOmExWUSm5WOjVtC1pi9HwtN4a8sV4rMKAXi9XUUclWbYNBQyoyD1Bjz/Hex9W+qgyViwdXkiY8nR5zDlyBSOJx4H4HzqeX4I/wGAqm5VrYF2u+B2T+R8T5vUuXPJ3bUb0WQiYN4XZfabdTr0kZHY1qljnTRIW7TIuj9t/gKcunYl85tvKDx/HoW9PcHfrUMTEPDYYxEUCtniS0ZGRuZXHItIZ/qWq7Sr4sXK9qUnb8XsOLwocYeoIsQCoHAsWVWedGSS9b+7n92Nh60HILltACTnJeORU9KnssCAorg6SDQYMMQnoA0tR+7efaXObYiJpuD0GfJPnESwscXtJUnAUuHggCUvj6IbNyi8IumtqAMDMefkYMnJwXyPm4VZp0MoTt1WOv8zUs1lZGRk/izkwFvmqVM/2JXBTYJxsFHhaKOmmp8TDUPc6FvXlk41fKS08sOfSkE3QPxZWNwc8lPBrbyUZv6E+PTspxxPPI5WqWVEzRFkFWVxK/MWThon1Ar1EzvPX4E5O5u8X/YDUHD6NKLFIgW/xZgyMoge8DzG+Hhcnh+AzzvvUHjpEgUnT4FKhcrdHVNKCnf6PouhOJXQ+523f1fQLSMjIyNzf2IzJEHKQDc7yLpRap8m9TJKQaqrtgAn7LTULdJj4+ZXph83G8m9w7N4NTy9MJ0CYwG5xlxCdCW12Za8PERDySSoITq6OPCW0sxRKsFsxhAdTdENaTyGmBhMqVIgb1O1KoaEeEyJSWSt+w4AbblyGOLjMeTcE+EDYmEhpvR0qVt5xVtGRuY/zp9mILxo0SLKlSuHjY0N9evX5+jR+3tE3mXdunXUrl0bOzs7fH19GTZsGBkZGQ89RuafQZ7exMtrzvHjeSm9XBAE3utZnclt/GH3W3ikn+OLAXV4s6ae6jufhe+eh6OSFzbVJXEX8lMlwbT+q0Hr+ETGZbaY0el1ACzrsIxXa7/KW43fYnWX1XzZ7ksqu1V+Iud5koiiSNbGjdY6vIeRs+NnRKMRAHNWFvrwcOs+i15P/NjXMMZLfyfZ6zdw57nnSBj/PwBc+vTBa5K0imK4cwcUCtyGD8e5V68nfUkyMjIy/2liM6XAO9jdDjKlSU6Dg1SG5WuKt7b7xc6WOW4uHLC3o3qlEjG1Op51ABheYzhQ4rpxI/MGqQWpAPjnlwiGWfJyseSVpLMb7tzBmJJK4cWLADgV13wbYmIoulkceMfGYkqW/MNVXl44tGgJQP6xYwBoQkNReXiUXJRCYa09F4udLuTAW0ZG5r/OnxJ4b9iwgfHjxzN9+nQuXLhAy5Yt6dKlC7Gxsfdtf+zYMYYMGcKIESO4du0amzZt4uzZs4wcOfLPGJ7MU8RiEfnfhovsu57CoVtp1u0KhYBwagmc+kpKKdfnws8TpNXt8F1gKoJyz0C/VVCjn3RQ18/Bp+YfHlNqQSp6sx6lQsmX7b7kp94/Uc+73h/u92mQf/wEyTNmkjBhIpb8fAD0UVEUnD1L0c2b6CMi0EdGYjEYyPnxR+kgtbRyn1/soS1aLCRNe4vCixdRODnhNWUKgkaD/voNTKmpCLa2uL8yCqeuXXAbOgTnvn0J3bEd78mT/nY17DIyMn8t8iT7HyemeMU7yM0OsqKljcFNS7UxKWy4qtX+n737DpOyOv8//p5etvelLL0KIk1pIqAGxW4sRI0VE/kSNYglElOUmJD4iwQ1grESjTEmMbaIBSMoiiggKArSYSnb++7sTn1+fzzLwLq7sMAuA8vndV1zMfPUeyDxzP2cc+7DFqeTdU4nloTs6L7qYDUA8c54guFgdHtVoCo6z7tLjSe6PVxdTaS6Ovo5sH07vhUrAHAPGID3VHMpTt/KlYSLzN5qgkF8q9cAZuKdededJF5wQfQarl69sGdkRD/bUlMbVTG3KvEWkRNcmww1nzNnDlOmTIkmznPnzuXdd99l/vz5zJ49u9Hxy5cvp1u3btx+++0AdO/enVtuuYWHHnqoLcKTNnD/G9/wyeZi/nbzCLIS961f/fCiDSxaV4DTZuXK4fuWMSEchJXPmO9risxe7l2fm+uRnvVLqCmGEbeYT8y//yR8bxYkdTrs+IKRIC+ue5HXt7zO5vLNOKwOXrv4NbokdqFHUo/Dvu7RZEQiFM6pHwkQDFK7di329HS2XnSxWfF9Pxa3G6OuDhwO0m64gZKnnsL36XLSbriBosceo3LhQrDb6fzoo8SNHEH82NPxrViBo2NH3CedFP0BlTVz5tH+miJynNj7kH3evHmMGTOGv/zlL0yaNIl169bRpUuXRsfvfcj+pz/9iQsvvJDdu3czdepUbr75Zl599dUYfIPYMwyDnfv3eJeZPd7O7mPgm39Hj6vMGMruyAYAOoVC0ETineBIoCZYE93uC/qiPd7Z1ft+7kWqazAcgejnwPbtWL1mYu4ZPBhnt24A0UJr0eutXAmYibctMZFOf/x/JJ53Hr4VK0g89xz8mzZFj7WnpBCpqyNSP/Tc4vFgVY0PETnBtXqPdyAQYNWqVUyc2LDK8cSJE1m2bFmT54wePZpdu3axcOFCDMOgoKCAf//735x//vmtHZ60gRp/iAXLtrOpsJoH3zIb6lA4wi9f+5rHF5vLg83+/smc3nu/YWjf/heq8sBW3xDvMIerMeLHMOoncPav9/2wsNoOO+k2DIOdVTu5+8O7eXjVw2wu3wyAy+aiU/zhJ/KxUPXOOw1+CNWuXk3V/z6ASARrXBy29HRsKSlY4+LMpBtI/N7ZJE46F6ivVr5gASXznwCgwwMPEDdyBGD2VqRcdRXx48Y16LUQEWnO/g/Z+/fvz9y5c8nJyWH+/PlNHr//Q/bu3btz+umnc8stt7CyPqE7EZX5glT5QwB0Ttmvxzt7ELWWfb3UCd2HscthtpdhYH1w31zq/Jp8AGZ+PDOahAOU+8s5t9u5LL5yMaeEO0S3m0PNG/Z4135lFknzDDoZZ7euTca6N4m2Z+5rIxLOnEDWz+7BGhfXqMd7/6HlGmYuItIGPd7FxcWEw2GysrIabM/KyiI/P7/Jc0aPHs2LL77I5MmTqaurIxQKcdFFF/HYY481ex+/34/f749+rqysbJ0vIIfss237hgkO7JhIRW2QW//+BUs3FWOxwMxJ/bhsWGcIh+DTxwALrH/DPGHMT2HbR7DzM3Alwpjph3z/gpoCakO1dEvq1mjf8+ue548r/wiA0+rk7lPvZlL3SQQjQWxW22F827ZX/eGHlP39JbMXIi6OzvMexxoXR+Gf5gLg6NqF4I5cfF+sxgiYvRYZM+4g9ZprAPNhQ2DzZuo2bCR+7OlYExKwJSURrqig8PfmOuhpP/4xyZd9PybfT0SOf3sfst97770Nth/sIft9993HwoULmTRpEoWFhQd9yN7e2/q987uzEl24CfJapJxwfByXpXSjypGOJ7ATAEdKDnvyzGlDD6elMPKbZ3iq00gA/nL2X7jl/VuwWWwNerw/uPID7FY76Z50ygtKqC9kbibdjn3FQ0NFRYTLywFwnzwIe2YmFo8Ho9ZcacTq9RKpn6cN4MjMbPK72DP2PVy3p6UStu1rY22JqmguItJmxdW+OxfUMIxm54euW7eO22+/nV/96lesWrWKd955h23btjF16tRmrz979mySkpKir5ycnGaPlba1dJM5B+ySwR25ZVxP5i3ezNJNxXgcNp744TB+fEZPMAx483Z4/354/9ewexVYbDD8JnPudkY/OHc2eFMP6d7hSJhr376WS1+/lFUFq9hdvZtp70+jwl9BxIiwdLc539Blc/HYmY/xg34/IMmVFF1u5VhjhELs+fl9VH/4oVnYZt06dt4yld3T7yC4cyf2rCw6PvggYPZ4137xBQBxo0dHr2GxWHD17k3SBedjS0rCYrXiHWH2bGO1knn3XWTcMf1ofzURaUeO9CG70+kkOzub5OTkAz5kb+9tfY+MOBbceCoPXDSAPSv+wi8z0ngtKQm8afg9+xJcX1wa5fv9Yvui4Avnl7nxAACWzUlEQVTqQubIpnhnPAC1odpo4t01sSvpnnQsFgtGOEywoCB6rjnHe1+CDua63daEBJzdumKxWHB23dfrHX9Ww+U07c0l3vsVV7OlpGJLVo+3iMj+Wr3HOz09HZvN1qjhLSwsbNRA7zV79mzGjBnD3fVVlAcNGkRcXBxjx47lwQcfpEOHDo3OmTlzJjNmzIh+rqysbHcN8vHi4/rEe+JJWfDS1dy75wv6dLqIYSPH0237/4P15WaxtG//aybbPSdA3pdwylWQ2NF8/eSzw7p3SV0JeTVmpdU7l9xJijuFzeWbeWjFQ/z29N/yxNlP8Pa2t+mT0ueYrFL+XTWfLidcUoItJYUOv/0teb/+Ff4NG/Bv2IDF7abz44/j7tcXi8cTHSpo79ghOievOWk//jEYEVJ+eC1xI047Ct9ERE4Eh/uQ/ZxzziEvL4+7776bqVOn8swzzzR5Tntv6xPdDsb3zYTizXzx9h8hM5k1TjtYLNS69g3d3lXfQ50YDuO2OiiMBFhTtIaRHUYS7zAT7+pgdXSoudfuBWDuqrlYikqZGNxXdC1SVY3F0Xi5TM/JA6NLTjq7dsX/7bfmPSdNovLNN6PHNTcdqcFQ87RUjHBo3+dkJd4iIq2eeDudToYNG8aiRYu49NJLo9sXLVrExc0sReTz+bDbG4Ziqx+iZBhGU6fgcrlwuVxN7pOjJ6+ilk2F1VgsMNa1ETa8hQW4jL/AW39pfMJFj8GQa1rt/pneTD67+jMueu0iCnwFlNSVkOZO47YhtwFgt9q5sOeFrXa/tlb53/8CkDjpXBLOnIA9Yz47rrsOo7aWjg/9Ac/AAQB4Bg3C95n5sCJ+zJiDVhv3DBxA5wP0KomIHIqj9ZC9vbX1LyzfwXvf5HNG7wwuOKUDHZI8EAnDf35EiWEmx4MzBrOzaic7OsSTUWwlJRJhd/1a3p1CYXp7M3kjXMJneZ/ROb4z9318HwA1gRo8dg9DMoewuXwzf/j8D/xt/d/os8tg/6o7kepqLM76xNtuh5CZILtPHhQ9Zu/DXHt2Nt6hQ6LbrQkJWL3eJr+bbb8eb3tqKkbdvikC361wLiJyImqTquYzZszg2muvZfjw4YwaNYonn3yS3Nzc6NDxmTNnsnv3bp5//nkALrzwQn70ox8xf/786FPw6dOnc9ppp9GxY8e2CFFaSai2hh8NhG3hLBJW/8nc2GWUWTitrgL6XQDpfaByD3QeDidf3uoxeB1e5p09jx8u/CHhSJhHznyE7Ljsg594FPm3bsXZpQsWe+P/yxmBAIEdO3B07kzVokUAJF5gPizwnDyQHm++QaSyEvdJJ0XP8QwdEk289x9mLiJyNByth+ztiWEYPPzeBsp9QZZuKuaP721gRI807khZxpA9X1CaYvYYp7pTuevDu1hXuY5H3C7OrAsxNGc8T7sexfjmP2xM7wrr/8ruqt0U1RbxdcnXAISMECenn8zzk57ngU8f4G/r/wZAemXDv9tw9b4eb1fv3vjXm0U7PYP2Ldfp7t/f3DZkMLbkZGzJyYTLy5sdZg5gS042544Hg9hSU4nU7JsXbktKPrK/PBGRdqBNEu/JkydTUlLCrFmzyMvLY+DAgSxcuJCu9XOG8vLyGqzpfcMNN1BVVcWf//xn7rzzTpKTkznzzDP5wx/+0BbhSSvK+eBW7tv8tjlXe+Vb5sYL5kJmvza754bSDfRN7Us4EiYYCeK2u+mT0oc3LnkDwzDoEN+41ySWKt99j90//Sne4cPJefIvDXoLgoWF7Pq/adR98w2OnBwiPh+OTp3wDBkcPcbZuXOja3qHDKEEwGLBO3Jk238JEZHv0EP2QxMx4I6z+/DgW+vomRHPt/lVrN64gxzXw2CB0p5nQMkKUj2puGwu1pWsY4fDDs5MkjzJjOg6AbpOoGiLOey7qrII//8+xBk0CDjMUU/VwWrcdjcZnn3DvrMqrUAEW1oa4ZISs8e7PvH2DBwYTbzdA/cl3gnfO5vOf34MzxCzt9vZvTu1q1c3qGj+XRaLBUdmJsHdu7GnZxDZrxCe5niLiLRR4g0wbdo0pk2b1uS+BQsWNNp22223cdttt7VVOHKEthXX8Pqa3azcXkZeRS2/unAA47q6YbPZQ8vKZ80/e551xEl3KBLCbm2iZ9gwmPflPJ748gkeHPMgXRK7MOXdKYzPGc+c8XOOuV7uvcr+8RJgroG669Zb6Tx/PlaXC/+mTeTecguhPeYc9eBOs3pt4gUXHHTouPe00/COGomrV2/sKSlt+wVERJqgh+zNe/PLPTz6v008fs1Q+mQlAGCzWrh+dDeuH92NcMTgoXe+JWXZg6RbKgmn9qIkrRuUrCDNnRYtALrDkwg9L2lw7VS3WYR04LubSXp/OeePs/LqaAv9UvsRipjDxvcvIJpT4waqcfXpje/T+sS7fuSBd+QIKl5/HWfXrjiy9vVmW2w2Es4+O/rZ2a0btatXN1vRfK/Me+6h9osv8JwyiFBxUXS7LUlDzUVE2izxlvZjU0EV3/vTRw223fzXFdycvp6fRULgToagD8IBcw3uI7CjcgdT3p3C9QOup0NcB2wWG6d1OI0vCr5gTKcx0R8V9y+7n36p/QhGglg4cJIaS8H8fHzLzSHhFo+HmmWfsuPqa0i5+moKZs8mUl2Ns1s3Ovz2QSpeex3/tq2kXH31Qa9rdbvp+txzbR2+iMgB6SF70257aTUAv3j1a/45dVSj/TarhZkTexBetQjCYDv3d5TueRcwE+tEl5mobu92GnxvFn9f/3c8dg8TcibQM7kndw2/i4FL3waK6VpocGbOmTxy5iM8+sWjvL75dTon7BsplVVtDud39+mD79Pl5lDz+sTb1asX3f/zCtaEAyfGcaNHU/Hqq3iGDjvgcYnnTCTxHHNG+f7Dy9XjLSKixFtaoFdmPKd0TiI1zsmZ/TJZvq2Ut77Ko2PJMrCDcfIVWE77EVTsgl5nHfyCB/Di+hcp8BXw0IqHottcNhf+sJ8hmUNYcO4CinxFvL7l9ei8thEdRhzRPQ/Ev20bdV9/Q+IF5x+0F3qvSCBA0cNz8AwZTHDXLjAMPMOHkXH77ey67XbqvvmGvPvMYjieYcPo/OfHsKek4B124B80IiJyfKkNhqPvv9xZTsQw6J2VQLzLDkUbsIX95sPr3hO5KjGVEdkjGJo1FH/YLEy2o3IHhmHw2OrHqA5W8/rFr9MjuQfXD7iebWVvUgdklxlsrV9SrLSulMLaQgakD8ARMjhtg0GnXeayY64+fYD64mr1c+utcfE4O3c66PdIuvAC4sedcUjrce/fy63EW0REibc0wTAMPt9Wyik5ybgdNiwWC/+cOgqX3Wyorx7Rlc5Jbs5euRYMsPQ6GzL6mq9DvM/yvOX0TukdHRZ39/C76ZHUg4XbFgJQ5CtiV/UuAIZnDcdqsfKbMb9hZMeRzP5sNrWhWk7vdHorfvuG9vzsXuq++gqr10PCfmuZGqEQRiSC1elsdE75P/9F6V//Cn/9K9Z488dQ0kUXEXfaafR443Xy7vsFNR9/TOL559Phd7/F2o4q9oqIyD4TT9pX4X3Ooo18uLGI2d8/matO6wIF35g7sgaCxcKp2adyavapAFQGzPnRxbXF5NXkRZcJ27+GSXDPHvP0Moi3xwFEj+uW1I3hr0U4dZMB+MFuxzNkqHliOIwRNh8I2BLiW/xdDiXphobJtjVRibeIiBJvaWBPeS03PreCDQVVnNkvk3nXDMXtsEWTbqgfIjfSBSsKwOqAboeX+D7x1RPMWzOPJFcSWd4sfnbqzzg1+1R+0O8H/KDfD4D6hwD5n7OlfAuX9zErolssFi7ocQHjOo+jKlBFx/i2KcoTrqqi7muzV73qgw9IOOssDMOg8r//peChhyAYIvv+X+MZPJiKV1/F2a0bCeecQ9kLL0SvYS7b4iTx3HMBcGRlkfPUk4QKC7FnZra4F11ERI4PhmHgtFsJhCJcMmRfb/LmQjMp7plRn+wWmO0L2QMbXSPRmUiqO5XSulKW7VkGQJo7DY/dA8C6nauwlJUBEOeHN1f/nY/3fEJulTmnvldVPL03GUQsYL/+SrpdeT3O7t3AYoH9Kshb4+Ja9bvvb/9EXet4i4go8ZbveOT9TWwoqCLOaSM7yU1lXRC3Y1/STcgPucthg9kjTZeR4Gr5E/O9Psv7jPlr5gNQ4a+gwl/BlPem8OHkD6OFY8BMskd0GNHkcPIEZwIJzoRDvndL1a5ZA5EIANUffYRhGOy5+57oWtsAu6ffAVZr9LiUq68msGMH1vh40qfeQuGf5pJ08UUNfoBYLBYczaxzKyIix7cqf4hAyGwT0uPNEU21gTC7y2sB6JlRn+zuTbyzBhCMBPkg9wNS3akMzRyKzWrj9iG347K72FllFt7slLAviX/ozbv52X73nOQYQsEnX3D75xH+fIGNrJ1mpXLXqNPo8bNfY7VYAbDGxxOpqgLA4vU2ucRla7F4vTh79CBSVXXAZchERE4USrwlqqjKz6urdwPw15tOY3i3VAjWwdevQJ9J4PTC2/fAqgX7TjqMOd0ltSX87KOfYWBwUc+LcNqc/Hvjv7n2pGsbJN2x5lu1Kvo+XFRM2UsvmUm33U7GrT8h4vdT8pcnIRLB0bkzwV27KPv73wFIvuwy0m6+meTJk6PDzUVEpP1z2qz8afIpbCqoptQXoJPTw9Zis7c7xesgLd5l9jrn7028B1JaW8pdH96FzWLji2u/AOCyPpdRUFPA75b/DoCJXSdG79HF52lwz84VNoaviNClCKa9FSbOZrZfGZdeHk26AawJ+xJvWxv2doP5kLn7f17BCIWbnJYlInKiUeItUS98up1AOMLgnGSGda1fomrpH+Gj/wenXAXn/A6+/Ie5veMQSOwEQ6475PvEOeK4uNfFLNuzjF+M/AUeu4fbhtxGiuvYWhardpX548fidGIEAhTO/j0ASRdfRHr9OrVJF14IhoGzSxdyp9yM7/PPwWIh5YfXAGBLaLseeREROfbsHSU2b8kWnJveYnrKp+zuYfZPR4eZVxeCrxgsVsjoR2nVDgBS3CnRRNkwDB749AGqglUMTBvINf2vid6jQ1XDn28Zu6rpUr96V7dCgCIsXm+D2iQAtrh4QvXvrUehfbK63W1+DxGR44USbwGgti5A5NPHOd+axPljfmzOPTYM+Po/5gFfvQzOOAjVQfbJ8KPF5lyxw+C2u7lj2B3cOuRWHFYHwDHV0w1gBALUfvUVAClXXUXpX/+KEQyCxULalCnR41w9ekTfd5r7J/bcfQ+eU07BmZNz1GMWEZFjQ5zT/Hl1duWrULKWrmUGcNN+87vXmn+m9gSnl9K6UvPjfm1hxIgwMH0gqwpW8eDpD2K37vvJllFh/hm2gM2ADss2YzUgZAW7OcqdxO+djdXrbRDX/sm2RmOJiBxdSrwFgG/ee5a7jL+CE4wlr0PSnyEuA0q3mAcYEVjxtPl++E2HlXQHI0HsFnu0oNjepPtYYEQihIqKonOva7/5BsPvx5aSQup115pVyoGEs89ukGzvz56aSpdnnj5qMYuIyLHn2/xKlmw0u5/jwmZ18r4l7zPYcga9MvsTioQo2f056YCtvrDa3sQ7zZ0WvY7NamPa4Glc0/8aklwNi5Ollpn91ps6Qr/d4K2oX37s1E4McvWg5uNPSJ78g0axWeP3DS+3KfEWETmqrAc/RNqjUDjCva98xVMfbQXglLrPATCwYCnfAf+eAmvM+cqk9tx3ojMeTr6iRfeIGBF+9cmv+NUnvyIUCTF/zXyuf+d61hatbdXvcrjCVVUY9dVdS597js3jxlP6txcBqP3CHGbuGTYUR6dOeIYOBYeDtB//OGbxiojIsW/hV3n8/TOzunhCpDK6fX7W65zeK43d1bs5e/tLDO7ehQ0p5qoc0R5vT+PRX99NugESSsxCbV93a/gQfGf3eHIef5xeiz/AO3RIo/P2T7aPxlBzERHZR4n3CSqvoo5/rNjJbxeuJ7e4Gsf2JQBYrvm3mWjXFMKyR82DT58Ovc423w+aDK6WNda+oI/Vhat5dfOr/P7z3/O39X9jdeFqCmsLW/8LHQLDMCh69FE2njaC4sf+jGEYlL1kzl0vmjuXYEEhFW+9BYB36DAAcp6YT8+Fb+E5ufGyLyIiInsVVQfq3xkkUF3/3kKH8lX0962koKYgeux/6xPzktoSoOXTrtzFZoG0td0a/oyr6d8Fi8PR7MoZ1vj9h5q3bXE1ERFpSIn3CSon1cvY3ukALPrfu+ArAWcC9BhnFlHby2KFvufBxfPgrF/B2b9u9pr5Nfm8v+N9akPmk/h4ZzxTTzGLkL284WVqQ7UMSh/EmTlntvr3MSIRcn/0Y7b/4CoC27c3fUwwSN26deTN/DnF8+aDYVD6wgv4Pvuc4K5dgLnu9vYrrsC/bj3WxEQSzz8PMNcj1bxtERE5mOJqc9i3Bz8ugubGIT80//z0cfJ3fxY9dqfFHDJeUtfyxDtSV4e93EzoczOgfO807vg43jG+ZvJ/Jzd77v7zum3x6vEWETmalHifwG4a0x2A0q/eAaA253SwOaDPOdCzvhJql9EQlw4JWTD2TnA3HvK218JtC7ljyR3cueTO6Lbze5zPpb0ujX6ePmx6dI53a/KtWEnN0qXUrlnDtsuvoGrx4gb7A7m5bDp9LNu+fxkVr70GViu2pCQiVVXsmTkTAGdPc0h9qNDske84+3dab1tERA7J3sQ7mRoADKuDD7Ovx7BYYcv/KFjz1+ixO2vNueCX97mcX478JWM6jjno9YN78gCwxHmpcUNhSn2bOrAPBf4iinxFzZ5rS9hvqLnmeIuIHFVKvE9AdcEwhZV1jOuTQbc0L+NsXwKwO220eYDFAhc+AoN+AN+b1eLrvr/jfQA2lW/i+rev58uiL4kYEe497V4mdp3IjQNu5NTsU1v9+wBUvPmGGbrLRaS6mt0/nU4wP3+//W8SrqjAGhdH3OhR5PzlCdKn/R8AoTzzR0zmjDuIP9t84JB6ww2NlmERERE5mL2J9+B0s7y44Unh+lcLeTs0HICC2uLosTurdmIYBkMyh3Bl3yvpn9Y/uq9u40a2XX4FJU83LNoZ3L3bfJOdARYLe7LNQqWBU/oA5pKdzbHG7T/HW4m3iMjRpKrmJ6DPt5Vy3bOfc1bPeO7ptomhX28CoOuIi/YdlJwD3//LAa+zIn8FS3Yu4YIeF7Cneg9ri82iafk1+eTX5PPDhT+kT0ofXrnoFR4e/3BbfR0ifj9V77wLQM78eRT9+XFqv/iC4nnz6TDrAQCqFy8BIOvnM0m+7DIAwqecQuHcRzBqa7HGxxM3dixxo0ZR++WXeEeMaLN4RUSk/Sqpn+P967M7wGsQdiUDsMA4n/P4nHz7vp9etaFaSutKSfOkNbhG3caN5N5wI+HSUoK7dpF6001YrGZfyd7EuzzFCcDbZyVx0aQbucD/MGA5cOLdYKi5Em8RkaNJifcJIr+ijs2F1XRNdZP4/l0sdX5Cp93FWHcbYIHKhF4kpnU7pGu+tvk13tjyBs+vez66bWjmULokduG1za8B0C+1Xyt+i6ZVL15MpLoae8cOeEeOJNPlYsc1P6T8lVdIm3ITFo+Huq+/BiD+jDOi59kSE0m6+CLK//EyCRMnYnU6wekkbtSoNo9ZRETagUgYcpdDx8HgjMMXCOELhAFIwiyAFqxPvLc4T4IeZ1NQt77BJXZW7WRFwQrS3GkMzhyMtaaO3BtvIlxqVjoPl5cT2LoVV69e5vXyzVFaK4xtAPz2wsdJSupO6KU5wIGX6rQ2GGquOd4iIkeTEu8TxPvrC/jFa18z1vYNLzhe2zfJIKkL9L+AxOFTDul6daE6/pf7PwB+OvSnPPLFIwCc2eVMOid0jibeIzuMbKVv0Fhg1y5qPv2U8n+8DEDSBRdisVrxDhtG3LgzqPnwIwrn/Im40805c+5Bg7BnZDS4RuZdd+Pq0YOkiy5qdH0REZEDWvsvePUWGDkNzp2NzWrhiR8Opbg6ALXvAeCzJQKQ6HXCVS9z5pfz6F61i9VFq8mvyWd14WrmrDKT5hXXrMD/xReES0qwd+iAPT2durVr8a1cFU28QwVmHZLaFC/gw8DA6/A2jq0JDZYTU4+3iMhRpcT7BJEe76RHehyXVHwIwKvhMZx2y3w6depizuk+RB/v/piaYA0d4jpw08Cb2FaxjVUFqzi/x/l47ft+AIzq2Pq9x+Hycor+/Dhl//gHhELR7UkXXRh9nzl9Ots+WkrVu+/i+9xcozx+/LhG17LFx5F63XWtHqOIiJwAyrabf+74BACX3ca5AzsA8MzsNUwBdtW5AUhw28Fm5/+G3g7AR7s+oiZYw9fF5oisk9NPxm13U7LV7Mn2nHIKrh496hPvlaT8wKxWvrcAaDAtEfBRVleG1bKvZI+B0Wy4+/dy2zTHW0TkqFLi3Y75AiG8TvOf+NyBHTi3dzzhh1ZBGL7N+QGXdu56WNddumspf1z5R/O63c/FarHy29N/2+CYVy56hWA4SLon/ci+BFD1wWLK/v53OvxmFo4OHdj10+n4PjOXY/EMHYq7f3+8I0dEewMA3P37k/mzeyj8/R8Il5UBkDB+/BHHIiIiEhWoX6e7cD2Eg+bKIPVSrWZV8+KwOec60d1wCPgZnc/AMAyeWvsUAJf0usS85DYz8Xb16I73VLMgm2/Vquh5oUJzHfBvLHsAKy+se4FxOfseLCc6E5sNd/+1u60JGmouInI0KfFuh77eXcGv3/iGFK+DBLeDUzonccXwHOLWvY4tXAtpvZn5o2sP69qzPp3Fvzb+C4AsbxZX9b2qyeP6pPQ57Pj3F/H5yPvFLwiXllLy7HOkXn+dmXRbreQ89STxY5pfeiX1+usJ7txF2YsvYu/QAVf//s0eKyIicsj89Yl3OABFG9hAVzYVVtE7M4FUi7kvP2iOAkv02KkOVFMTrCHdk47NauPb0m/ZVLYJp9XJOd3OAfYl3s7u3fGccgrY7YTy8gju3o2jUyeCheZyYWXx5mi1z/LNB9E9k3qypWILNwy4odlwGww1j1OPt4jI0aTEu50xDAOP08YXuWUY9aPN3lqbxxXDc2DN380Ng686rOHlYBZLs1qsXHfSdUw9ZeoBq6e2hrKX/hEtMFP51lvY01IB8J522gGTbgCLxULWz2fi6t0b94CT2mT9cBEROYHt7fEGyP+Kd4pd/On9jfzg1Bwm16/jnZSawW9OH0jnZA+Ldy7m5x//nFEdRvHYWY9x54d3AjChywSSXEkA+Pcm3t26Y/V6cZ90EnVffYVv1SoS0tKIVFQAMKDfWHaVfMwFPS4AIM5ptsfVwf1i+g5rUhIWrxcMA1tyUuv+XYiIyAEp8W5HavwhLnn8E847uQPj+mSwZIP5VHxsr3TiVjxmzkGzWGHQ5INeKxQJsWjHIjK9mfRJ6UOC0xySdkWfKxieNZweyT3a9LuA2dtd8swz5geLhXBpKcVPmkPyEs89t0XXsNhs0XlxIiIirSpQs+993lcU+wcBkB7vIsmoBMCTlMGFI82pXU99lQ9ApjeTulAdO6t2AnB+9/MBCFdWEi421/l2du8OgHf4cDPxXrESz+DBAFjcbn79vYeYsPsjxueMByDbm01JfEmD+d7fZXU66fL004CB1eU68u8vIiItpsT7OGcYRrQn980v97CpsJo3vtzDH684hSUbirAQ4VbvInj/D+YJZz8ASZ0Pet0nvnyCv3xlruPtsXtYdPkiklxJWCyWNku6DcOgbu1a3CedhMVuj/Z2O7p0If700yn7+98xfD6w2UiY+L02iUFERKTF/FX73uevpdBeB0Bmoou4iNnzXGHsm0td4DPnZ2fFZZHkSuKqfldR7i9nbOexwL5h5vbMTGz187E9QwYDUPfNN4QKzPPtWZkkuBI4v8f50Ws/PP7hFoXsHTrkUL+liIi0guYfi8ox7+UVufT75Tt8stl8Ov7vVbsAmHxqDsNyErmvdy7vxf2aIevqk+7TZ8CY2w963dK6Ul5Y9wIASa4k/GE//93631aPv27dOjaOHUvRo48BUPbCC2y/cjIFv/8DRiRC2UsvAZD2o5tJ+v73o+fFjRiBPTW11eMRERE5JA2Gmq+lsLI+8U5wExc2h4Qvz4+wbEsxxdV+8mvMHu/suGwAfj7i5zx0xkPYrWY/iH+/+d177S0c6t+6lWC+mXg7MjLb8EuJiEhbUI/3cexnr6wF4JqnP2PFfWezKtes3n1F4jp45BJ+VJFrHuhMgHH3wOjbWnTd575+Dl/Ix0lpJ/GP8/9BIBLAZWvdIWmGYZD/mwcJFxVT8swzpFxzNaV/fR6A8n/+E88pgwju2oU1IYGkCy7A4nbj6t0L/6bNJJ43qVVjEREROSz7DzX3V2CvyAWSyExw4g2bveFLd4X511Of8afJp0QT7yxvVtOX27YdAGePfYm3MycHi8OBUVdH7erVANizmj5fRESOXUq8j1OG0XCdzje+3INhwCkd40lbdCP4SsCdDEN+CKffAXEtW9ar0FfIS9+aPc23DbkNi8XS6kk3QNXbb0d/QBh+P7vvvIvg7t3m50CAvF/8EoCkCy/E6vEA0PHhh/EtX07SpZe2ejwiIiKHbG9Vc5sLwn4yfRuBU8lyBbEaIQByOnUif3cdbkeYrRVbAeiW2K3JywW2mvtd+/V4W+x2nN2749+4kZpPPwXMoegiInJ80VDz41R+/XC2vX7z33UA3NAx10y6vWkwYz2c89sWJ90Ai3MX4w/7GZI5hDEdD1w1/HBF/H4K/2jORXMPHAiAb/lyAFy9ewNmMg6QfOUV0fPcffqQet11WGy2NolLRETkkNQPNTc6DQWgD9sBSLf7zP12N8V+s80qCG4gGAmS6c0kJyGn6cttbzzUHMDVq6e5f+8c8Cwl3iIixxsl3scpj8PGby4eAIB1v1Wyxoc+Md/0vwic3gNeo9BXSE2wpsG2yf0m8+w5zzLztJlttvxW+T//RXDPHuzZ2XR55mls6fseDHSa8zDObt0AcJ8yCHe/fm0Sg4iIyBExDIz6Hu9ltWYifUWPML///sm4AuUARNwpbC8x29luiTncMugWJved3GT7aoTDBLbvAMDZvWERU2f9PO+9HOrxFhE57mio+XEq2evk2lHduGhwJ6rqgjz2v81szi8jecc75gEDDjwce2fVTi574zKSXEk8O/FZOiV0ii5Bcmr2qW0WtxEIRJcIS596C7akJFJ+8AOK//xnvKeeiqt3bzLv/Rn5D8wi47aDF4ITERGJiWAtFiIALNwTzxgHdLSW8YPTusCWzQBsqLSzd2ZY3/RujO95a7OXq3j9DYxgEIvbjaNjhwb7XD0bJt4aai4icvxR4n2cS/I4SPI4+MPlg2Dz+/C3MojLgK4HHib+4voXqQ3VUhuq5ab3biLBmcAvRvyCoVlD2zTeijfeIJSfjz0jIzpXO+3HP8KWnEzCmRMASBg/noTx49s0DhERkSOyX0XzElcORIAqs3gavlIAyvdbSizR7Wj2UjWffU7er38NQNpNN2KxNhyQuHeo+V4qriYicvzRUPPj1H+/2sPaXRUEw+bTdqoKYOmfzPf9LwJb889UqgJVvLrpVQBS3ank1+SzqWwT87+c36YxG6EQxU89Zd73ppuwusyibVank9QfXoOjY8c2vb+IiEirqV/Du9pwM3iAOfUrVLGbLUXVUGuuMlKOuRa33V3IZ/kfURmobHQZIxBg94wZEAyScO65pN/auFfc2aUL2Pe16/aMjFb/OiIi0rbU430c8gVC3PbSakZavuH5ru+AxYCSzeCvBIsNhl57wPP9YT/n9zifdSXrePTMR3lw+YN0TujMLYNuOeLYqpcuJbgnj+Qrr2g0h634yScJ7sjFlpxMyn5F00RERI479UuJ1eDGX788mD3k47+ff8tP483Eu8yIB+D0wdv46ZI5XNrrUmaNmdXgMqGiIsIlJVgcDjr+fnaj3m4Ai8OBs1tXApu3YEtKwup2t+U3ExGRNqDE+zj0bX4VhgE/df8XR96X+3Z0GAwXzIGOQw54fronnV+N+hURI4LVYuXRMx9tlbjCVVXsuu12jLo6HJ07ET9m33B33xerKX58HgBZM+/FGhfXKvcUERGJifqh5jWGm5V7/FRb4og3aujqqIz2eFcQj8tuJejYAjRdQyVUXg6ALSXlgAm1q2cvApu3aH63iMhxSon3cSIcMdhZ6mNDQRVvfZWHgxBD2GDuvPARSOkO3U4Ha8uX2tpbTK21VL61EKPOXOas7PkXoom3f8sWdt91J4TDJF54IUkXX9yq9xURETnq6iua1+Bm6aZi9jiT6WOtoZOtDKrNOd5lRjz+cIiNZRsBGJg6gIjPh9W7b9WRcFk5ALbk5APeztWzJ1VofreIyPFKifdx4Js9FVzz9GeU+4LRbcMtm3EZdeBNh6HXwwGW/lqcu5h8Xz6X976c33/+ey7rcxknpZ3U6nGW/+c/0ffVH35IYPt26r79lryf30fE58PRtQvZv/5Vq99XRETkqKvv8U5JSYUCyDdS6cNuMiiF0q0A5BlpWJ3F1IXr8Ng92H7+RzZ9/jk933k7Ok87vLfH+yCJd8L3zqb8P/8h8dxz2uwriYhI21HifRzon53Im7eezox/rqE2GKZPVgLX1H0MWzF7uQ+QdJfWlTLjwxmEIiH+vv7vbK/czqIdi3j/ivdx2pytFqN/0ybqvvoK7HY8J59M7erV7LjxJkJ5eQB4R4yg05yHscXHt9o9RUREYqY+8ba7zcrlBUYKACmhIij6FoANRg5Wt9kO9ovvSc3HH0MwSN23G4j/buKdknLA27n796f3ksWt/jVEROToUFXz44DVaqFjsod//HgU/71tLHOuHMywyFpzZ/exBzz3zS1vEoqEANheuR2A6wdc36pJd3DPHoqfNKuVx48bR/qtPwEwk26rlbRbbqHLM09jT0trtXuKiIjEVP1Qc3dcEgD5pAIQX/ylWezUaufis07H6jIT7+G+LAiaI9dCRUXRy7S0x1tERI5v6vE+Ttis+/VqB+tg5+fm++7jDniey+Yiw5PBmV3O5MNdH+K1e7mq31WtEpNhGOT/+n7K//nP6Lbky75P3OjRJJ53HsGCArJ+dg+eQYNa5X4iIiLHjPqq5h/tqAX29XiHd3zM7VkZfOT14N4zFZvbnJPdv2jfA+9QcXH0vRJvEZETgxLvY1woHOGnL6+hZ0Y808b3xO2wwa7PIeyH+GxI63XA83/Q7wdc3udyIkaE+0bcRygSwmFztEps5f/4h5l0Wyy4B51MwplnET9+PBaLhU5zHm6Ve4iIiByTAuY63oV+86dUvmH2eG/Bz0de832dUUGw4jzGdzqHbhs2Eqo/tUGPd5lZAV2Jt4hI+6bE+xi3o9THW1/l4XHYmH5Wb3PjpkXmn93HHnB+9152675/5tZKuuvWraPgd7MByLz7btJuurFVrisiInJciFY19wCQX9/jvcHZcCqXxV5JZfGZ2De9vy/xLtZQcxGRE43meB/jNhWYDXuvzHisVovZ0H/xV3Nn/4uaPc8X9PHRro+i87tbkxEMsudn92IEg8SfeSapN97Q6vcQERE5ptUPNa823PxobHdummQuobnB2fABtz1uMz1SXPg3bIhuCxc1MdQ8Jblt4xURkZhS4n2M21xoDmXrnVlfDfyL56GuAlJ7Qr/zmz3v3e3v8pP//YSb37u51WMqe+kl/Js2YUtKosNvH8TSgl53ERGRdqW+qrkPN98f2pnvnz4YrPZoj/cPu18IgD1+Axdn7MQIBKKnNlVcza4ebxGRdq3NEu958+bRvXt33G43w4YNY+nSpc0ee8MNN2CxWBq9BgwY0FbhHTc2F1ZjIcKZttWwbSl8+ri5Y/RtYLU1e96rm18F4PROp7dqPKGiIooefQyAjBkzsB9k+RMREZF2yW8+GK823Gb9FasVIz472uN9Yf+ro4euWPoCAPYss9CaiquJiJx42iTxfvnll5k+fTr33Xcfq1evZuzYsUyaNInc3Nwmj3/kkUfIy8uLvnbu3ElqaipXXHFFW4R3XNlUWM1ltqVc8PV0+OsFULkL4jLhlOYrk2+r2MbqwtVYLVYu6tn8cPRDFSotZddPpxOprsY9cCDJl1/WatcWERE5rtQPNa/Bg8tu/pwqSMyk0mbDbkCv1D5ce9K12Cw2xvlyAIg/4wwAIjU1RHw+jECASI15HSXeIiLtW5sk3nPmzGHKlCncfPPN9O/fn7lz55KTk8P8+fObPD4pKYns7Ozoa+XKlZSVlXHjjSd2wa5IxGBLUTXfs64yN7gSwWqHCT8Hh7vR8YZhsKF0A4+vMXvFx3YaS6Y387DuXbdhA/m/eZCiRx+l/LXXKH7yKbZfOZnaL77AmpBAh1kPYLE13+MuIiLSnhn1Q82d3gQ8DrM93Oo11/TubvPitDm5c9idfHLVJ7jXbwfAe9qpWNxm+x0qLiZU39uN1Yo1MfGoxi8iIkdXq1c1DwQCrFq1invvvbfB9okTJ7Js2bIWXeOZZ57h7LPPpmvXrq0d3nGlsMqPNRJitP0bc8N1r0OHwWBt/LzEMAxuX3w7S3YuiW77fu/vH/I9jVCIwj8+TOkLL0A43Gi/IyeHnCfm4+rZ85CvLSIi0l5Y6quaz7vxDIgz53WP7nw6ny5eQvHE+wGwWW3YNuTiX7ceHA7iRo/Gnp5OcNcuQsXFWOPN+i22pCQsTbTtIiLSfrR64l1cXEw4HCarfh7TXllZWeTn5x/0/Ly8PN5++23+/ve/H/A4v9+P3++Pfq6srDy8gI9h2Uluvro5Fftfa8GTCh1OaTLpBrBYLJzX/Tw+2f0JIzqMYHzn8UzImXDI9yx76R+ULlgAQPxZZ2FPTSGQuxN7ZiauXr1IvvIKzesWERGpX8cbZ8K+baffQXy/84lP7xvdVP6vfwGQcNZZ2NPSsGdkmIl3UTG2kLnyiIaZi4i0f222jvd3K10bhtGi6tcLFiwgOTmZSy655IDHzZ49mwceeOBIQjxmFVTWkeRx4HbYsG9bYm7sMb7JYmr+sB+XzQXApO6TGNFhBKnu1BbfK7hnDzt+eC3eESPo8LvfUv7PfwKQededpN3c+hXRRUREjnuGEZ3jjSt+33arDTL7Rz9GfD4q3ngTgJTJVwJgT08H6iub1/8sUuItItL+tfq4pvT0dGw2W6Pe7cLCwka94N9lGAbPPvss1157Lc765TiaM3PmTCoqKqKvnTt3HnHsxwLDMJj+jzWcPedDVu0ohS0fmDt6ndXo2EU7FnHxaxdT6CuMbjuUpBugeP4TBPfsoeLVVyl54gn8mzZhcbtJvvLKI/oeIiIi7VbIDxGzt/pH//g2uvn3n/+eWZ/OYmel+ZukcuFCItXVOLp0wTtiBAD2jPrEu7hIFc1FRE4grZ54O51Ohg0bxqJFixpsX7RoEaNHjz7guR9++CGbN29mypQpB72Py+UiMTGxwas9WLSugLVbd/JgzSxOWnIL7PnC3NGj4bDxIl8Rv172a3ZX7+bfG/99WPcK7t5N+auv7rvmI48CkHjOOdjayd+niIhIq6svrAawJj9AzaefsumMcex8+1X+tfFf1NRVUfyXJ8l/YBYAyVdcHp3Dbc/IAMziauHyCkCJt4jIiaBNhprPmDGDa6+9luHDhzNq1CiefPJJcnNzmTp1KmD2Vu/evZvnn3++wXnPPPMMI0aMYODAgW0R1jEvEIrwu4XrmWT7nPHW1bCtfkdGP0jqFD3OMAweXP4gVYEqTko7iR8P+vFh3a/4qacgFMJzyin4t20jUj9PPvlKLeMmIiLSrPrE22e4cDgcVC9ZQqiwkL7fWviomw3vy+9Q9PhTAMRPmEDqD38YPdW231BzW4L5kFuJt4hI+9cmiffkyZMpKSlh1qxZ5OXlMXDgQBYuXBitUp6Xl9doTe+KigpeeeUVHnnkkbYI6bjwny92sb3Ex3TPFjCArmMgPhOGXNvguP/l/o8Pdn6A3WJn1uhZ2K0t+2c0QiFqv/ySmk8+ofqTT6j7ai1gzueuXfs1hQ89hLNnTzxDh7b2VxMREWk/6iua1+DC7bARKiwDIL4W7BY7fGUOP0/78Y/JuGN6gxo3e3u8w0XFhFPTALCpaKmISLvXZsXVpk2bxrRp05rct6C+avb+kpKS8Pl8bRXOMS8UjjD/wy0AjPdsBR8w+jboO6nBcYZh8NRa8yn6jQNvpG9q3+9eqkkRv58dV19D3TffNNiedPlleE89Fc/QodgSE/AMGdKiIngiIiInrPoe7xrDg9Nujc7VjquDVE8q4QpzCLnnlEGN2lR7+r6h5vb62je25KSjFLiIiMSKFo08Rry1No8dJT66eepI9tWPMc8Z0ei4L4u+ZF3JOpxWJ9eedG2j/c0p+/tL1H3zDVavl8TzJtHht7+l15LFdHzwQQAsNhvJl1+u9blFRKTF5s2bR/fu3XG73QwbNoylS5c2e+wNN9yAxWJp9BowYMBRjLiV7E28ceN22AiXlQMQX2uQ7kknXGF+tiU1TqijxdVKSgiXlprHaai5iEi7p8T7GLFiu9n43tm/3NyQ3ge8jSuUL9y2EIDzepxHirtlQ9PCVVWUPPEEAFn3/ZxOc+aQfNn3cWRnH3ngIiJyQnr55ZeZPn069913H6tXr2bs2LFMmjSp0VSyvR555BHy8vKir507d5KamsoVVxyHdUX8+xJvl91KuKx+qHkdZHgyDlg0zZ6aChYLhMP4t21r9jgREWlflHgfIx685GT+M20030vYbm5oorcb4Gen/oxHJjzCjQNubPG1S559lnBFBc6ePUm6+OJWiFZERE50c+bMYcqUKdx8883079+fuXPnkpOTw/z585s8Pikpiezs7Ohr5cqVlJWVceONLW/Pjhl+sxhp0OYlxevcl3jXQoYrNVqstKmE2uJw4O5vrvV9oONERKR9UeJ9DBnaJQV33grzQ5eRDfYZhgGAzWrjzC5n0iO5R4uuGfH7KXv+BQAypv8Ui73NpvWLiMgJIhAIsGrVKiZOnNhg+8SJE1m2bFmLrvHMM89w9tlnRwuvHvM2LYKy7eb7crNXf8zQU5g/+WQiNTUAeANwa7frYW+b3czSnJ0fexT3oEHRz/a0tLaLW0REjglKvGNoU0EVM/65hqWbiswNIT/srl+3O2df4r2neg+Xv3k5edV5h3wP3/LlRGpqsGdlkXD22a0RtoiInOCKi4sJh8Nk1RcH2ysrK4v8/PyDnp+Xl8fbb7/NzTfffMDj/H4/lZWVDV6xUL1jNbx4OfnPXGVuKDGLoZLak1B9YbW9PAXmZ2tcHBans8nrOTp1otvfXiBj+k/JuOMOJd4iIicAJd4x9PqaPfzni938ddkOc8NXL0PYD950SNtX5Oz1La+zsWwjv/3sty26rm/lSkpffBEjFKLq/f8BkHDWmapWLiIireq77YphGC1qaxYsWEBycjKXXHLJAY+bPXs2SUlJ0VdOTs6RhHvYvllnLr+ZVrUBwkEo3WruSOsZLay2V2C72aY3VVhtfxank/SpU0m/5cetHq+IiBx7NO44RgzD4M2v9gBw4SkdzGFr7/zc3DnmdrPwSr11xesAGNlhZKPrfFfFG2+wZ+bPIRzGCASpWrIYgPgzz2rlbyAiIieq9PR0bDZbo97twsLCRr3g32UYBs8++yzXXnstzmZ6hPeaOXMmM2bMiH6urKyMSfLtDJo97Q5L2Oztrk+8ZyyqZIx3PSftd2zZZnPZTs3bFhGR/anHO0a+2lXBjhIfHoeN7/XPgNdvhUCVWVRt1K3R4wzD4OuSrwEYmD7wgNes+O9b7PnZvRAOA1A0Zw7homKs8fHEnXZq230ZERE5oTidToYNG8aiRYsabF+0aBGjR48+4LkffvghmzdvZsqUKQe9j8vlIjExscErFsI1pfs+bF8K/koMLLy1201lfnGDY0M7dwFKvEVEpCEl3jHyxpdmb/fZJ2Xh/ehB2PYh2D1wyXyw2qLHFfoKKa4txmax0Te17wGvWfzEfDAMkidPxtW3L0YwCED8GWObnWcmIiJyOGbMmMHTTz/Ns88+y/r167njjjvIzc1l6tSpgNlbfd111zU675lnnmHEiBEMHHjgh8nHlNry6Nvguv9iAO8mZBOwBfDWVTU41LqrAABb8oGHmouIyIlFQ81jwB8K8/aaHfS37OA25xfwySPmjoseazC3G4j2dvdM7onH7mn2muHycgKbzWIvGT+9Hf/GjeTeYC7RomHmIiLS2iZPnkxJSQmzZs0iLy+PgQMHsnDhwmiV8ry8vEZreldUVPDKK6/wyCOPxCLkw2apLYu+t+V+wt9JJONVC+cPehKvd0KDY0O5O83j1OMtIiL7UeIdA89/sp1nAnfT37UT1tZvHPczGHRFo2O/KTbnig1IG3DAa/pWrwbA2a0b9tRU7CNHknrTTfg3bCDhzAkHPFdERORwTJs2jWnTpjW5b8GCBY22JSUl4fP52jiq1mcLlEffWyNBVpZn8ePiCN/7dg+e/tUNjo3Ufz/rQYqriYjIiUWJdwz08VbR37oTAwuWDoOgzyQYf290f3FtMTOWzGBUx1HsqTGHpB9sfnftF2bi7Rk2NLot65672yB6ERGRE4sjsG8ZsxDgMmdy0akEamvMfXUOcAf3nWNXj7eIiOxHiXcMjEutL9KS1gtu+ajR/r9+81dWF65mdeFqfj/299w9/G4cNscBr+lbba7/7R069IDHiYiIyKHJ8fih1ny/1uXEHTDfx/nBmmcOp89PgW6F+87RUHMREdmfiqvFQtFGACwZjYulVQYq+dfGf0U/z1szj0RXIonO5iu5GoEAdWvNueCeIUq8RUREWlOiYRZQCxg2PvF4cIaM6D53rrm0WF5qw/XLNdRcRET2p8T7KKqoDfLfr/ZQlmsmyaT3bnTMvzb8i5pgDb2SezFl4BT+OumvOKwH7u2uW7cOw+/HlpKCs3u3NohcRETkBFZf1fxrozsTfD6GVgeiuyyGmYSfM/bGBqdoqLmIiOxPQ82Pom/2VHDr31fzatxqUgDSG/Z4B8IB/rb+bwDcOPBGLup50QGvFy4vp/K99/B9vgIAz5AhWCyWA54jIiIihyASxqirwAKsiPTllsBmUgIOKr5zWFyP3lTu91lDzUVEZH9KvI+irUU1APQwdpsbMvo02L+reheJzkSsFiuTuk066PXyHniAqrffiX72Dh3SesGKiIgIQV85DsxebcdJF8DGtzCcqUBpg+Oc3bo2+Kyh5iIisj8l3kfR1qIaEqkmKVK/Hmh6w8S7R1IPXr/kdcrqyg5aTC24Zw9V7y0CIH78eKyJCSRddlmbxC0iInKiqi4vIgWoMVxMvGA8H+96krSH32T/xDtisfBI/stcsneDxYItsfnaLCIicuJR4n0UbSuuppfFXB6MxE7gSmjyuBR3ykGvVfb3v0M4jHfECHKemN+aYYqIiEg9X3kxKUAl8XxesJxfr3iQh4sTyNnvmGoP/CP/v9HE25qYiMVmO/rBiojIMUvF1Y6ircU19LTWJ97fKaxWUFNAbai2RdeJ1NZS9q9/A5B63bWtGqOIiIjsU1tVAkC1NYE9FeYsbnvAaHBMpccgYrVgiY8DwJasYeYiItKQEu+jJBCKsLPURy9L/fzu7xRW+8OKPzDu5XH8d+t/D3gd/5Yt7Jn5cyIVFTg6dyZ+/Pg2ilhEREQC9Ym3z5bAwq/NNbuttSEAQvW/oqo8YLfasSclAyqsJiIijSnxPkpyS2uIGNDXlmdu2K+wWk2whqW7llIbqqV7Uvdmr1Hz6adsvfAiqt4xC6qlT5umoWwiIiJtKFhtzuWusyfidoUBsAcjAOzMMI+p9lhIdaViqy+oZlNhNRER+Q7N8T5Kuqz6PVvinsYWrjM37Nfj/caWN6gL19EtsRsnpZ7U7DXK//0KRCJ4hg0j86478Q5RFXMREZG2FPGZBVGDjiTczggEwO43E/DVPS10LzDYnmXWZ7ElmwXV1OMtIiLfpcT7aDAMnGueh71Jd2Jn6DgYgIgR4cX1LwJwdf+rm12H2zAMfCtXApBx261KukVERI6CHI8fgI4dOuC0m0PMnQEz8X437fukznHw78J/McKdgjUpHgBb/ZBzERGRvZR4Hw1V+eCvBIsVpn8N8ZlQv1zYx7s/ZkflDhIcCVzc8+JmLxHcuZNQQQE4HHhOOeVoRS4iInJCS7f5AOiR0xlnVT4AjpCZePuC/YnrGsYospDiTsHRwRx77ujYMTbBiojIMUuJ99FQvBGAmrguhByZJNUn3REjwjNrnwHgsj6X4XV4m72Eb8UKADwnn4zV42njgEVERASA2nLzT08yV3Q6jaVrLNgjbwDw6a/Px56cxGV9LiMUCRF3cgRXzx4knHNu7OIVEZFjkoqrHQVrv/wcgGUVqRRV+6PbfUEfeTV52Cw2rup3VZPnhsrKMCIRfCvMYebe4cPbPmAREREBoKaiyPzTlsj3epxOTnhsdN87BYt5ZdMrxDniSHIlYU9JIfmyy7DVLysmIiKyl3q829jGgiq+WrOCky0Q3/kkemXGR/fFO+P5wxl/YFfVLjrGNx6W5lu5kh3XXY93xGkEduwAwHvqqUctdhERkRNdaXEBccBXxTAK+L8RneBFMGxWfr78lzhtLi7rfVmzNVpERERAPd5t7levf03XyC4AThs+stH+IZlDuLDnhU2eW/zkkxCJ4Pt0OaE9eWCz4VFRNRERkaPGE64CwJWQxtfFX9MtzVwWNGBzgsVCIBLgZ0t/xuayzbEMU0REjnFKvNtQtT/Eiu1l9LSajbQtsx8AK/NXMn/NfFYXrm72XP/WrdR8tBQsFixec+63e8AADV8TERE5ihKMagDcien8Zvlv+OX/7gGgxuIAwwbA29vepiJQEbMYRUTk2KfEuw19vq0ET6SGDpZSc0N6bwCW7FzCvC/n8dbWt5o9t/SFFwCIP/NMujz9FO5TBpF2041tHbKIiIjsFazFRQCAuKR0/CE/rqC5y29zYonsK4qa4k6JRYQiInKc0BzvNvTJ5hJ6WMzebuKzwJMMwLrSdQAMSBvQ5Hmh0lIqXnsdgNTrrsM7dCjdX365zeMVERGRfYI1pTiAkGElMSmVunAdqUEDgDq7k3A4gtXs9CbVlRq7QEVE5JinxLsNnd47nd55tbAbSO8DmEuIrS9ZD8BJaSc1OscIhdhz190YtbW4TuqP9zQVUxMREYmF6vJiUoBKvCR5ndSF6vbr8XYAddFjE12JMYlRRESOD0q829CEvpmws64+8TaHmedW5lIdrMZlc9EzuWejcwrn/ImaZcuweDx0nD1bVVJFRERipKa6ihSgDjepVgv+8L6h5nU2Jx6nn72LhFotmr0nIiLNUyvR1oo3mn+m9wVgXYk5zLxvSl/s1n3PPYxwmII/PETps88C0HH273D37Xt0YxUREZGoeHsYAK/XLGxaF6rDXZ94D+iZxbUDrwAgwZEQk/hEROT4oR7vNvLRxiJSnREG7PgEC0CWOZ97b+LdP61/9FgjEmH39OlULXofgIw7Z5B47rlHO2QRERHZT7LDTLyTExMIRoKEjBDO+sQ7LS2RPqk9AOiX1i9WIYqIyHFCiXcb+eN7G+i9500edpZBUhfoOhqATeWbgIaF1WrXrKFq0ftYnE46/n42ieedF5OYRUREZD+h+oHkdhcAdw2/i4xtHwHmlLBzu53LuM7jqAvXNX8NERERlHi3CcMw2FZcwyz7InPD8BvYW/Z03lnz2FaxjXRPevT4mo8/ASD+rDOVdIuIiBwjKqqqSAL8OHBZHVw/4HoKF5VRwjKsbg8WiwWvw4vX4T3otURE5MSmOd5toLQmQFf/RgZbt2BYHTDkuug+m9VGr5ReJLuTo9uqP/kYgPjTTz/aoYqIiEgz1u4oAGBLaSi6zaitBcDq9cQkJhEROT6px7sNbC+p4WrbBwBYTroY4jMwDIPaUG2jp+Lh8nLq1n4NQNyYMUc9VhEREWla0G8m2RG7G1/Qx6byTbjLzWTc4nbHMjQRETnOqMe7DWwtqmG09Rvzwyk/AODLoi+Z8M8JPLTioQbH1iz/DCIRnL164sjOPtqhioiISDMiATPxxnCw/Uc388JvruazbR8BYPVoeLmIiLScerzbQH7+HrpZzSfidB4OwL82/gtfyEelv7LBsTV7h5mrt1tEROSYEg7UF00rCmFZvpqLEyEvx+yzsHrU4y0iIi3XZj3e8+bNo3v37rjdboYNG8bSpUsPeLzf7+e+++6ja9euuFwuevbsybP1a1ofb6x7VgNQ7u0KnhR2Vu5k4daFAFzZ98rocUYkQvUnZmE1DTMXERE5tkSCZuJtCZo/l1KrIK4+F7d4NMdbRERark16vF9++WWmT5/OvHnzGDNmDH/5y1+YNGkS69ato0uXLk2ec+WVV1JQUMAzzzxDr169KCwsJBQKNXnsse6SzHzYA5ZOwwCY9+U8QkaIMR3HMChjEGBWPi/4/e8J7cnD4vXiPfXUWIYsIiIi32HUJ97WoMX804D04oD5XkPNRUTkELRJ4j1nzhymTJnCzTffDMDcuXN59913mT9/PrNnz250/DvvvMOHH37I1q1bSU1NBaBbt25tEdpR0anGnN+d1Gskm8o28dbWtwC4behtgJl0lzz5FGXPvwBAh1mzsOrJuYiIyLElVN/jHQKjflNiRRDQUHMRETk0rT7UPBAIsGrVKiZOnNhg+8SJE1m2bFmT57zxxhsMHz6chx56iE6dOtGnTx/uuusuauuX7DiuGAbsXmW+7zScx9c8joHB97p+jwFpAwjs3EnuTTdR9Kc/AZB5zz0kXXB+DAMWERGRpvRNd/BmvJeqkL/RPj0wFxGRQ9HqPd7FxcWEw2GysrIabM/KyiI/P7/Jc7Zu3crHH3+M2+3m1Vdfpbi4mGnTplFaWtrsPG+/34/fv68hrKysbPK4o23P9g109JVgWB2UJndk6S5zbvtPBv8EwzDYefOPCOzYgcXlIuOO6aRef32MIxYREZGmWBOC/NySzi0rcjnrO/ssbiXeIiLScm1W1dxisTT4bBhGo217RSIRLBYLL774IklJSYA5XP3yyy/n8ccfx9PEU+XZs2fzwAMPtH7gR2j7Vx/SEdhq70HP+A68fdnbLNuzjJ7JPalbv95Mur1eerz2Ks5m5ruLiIhI7MWFzVoz8XWN91m9SrxFRKTlWn2oeXp6OjabrVHvdmFhYaNe8L06dOhAp06dokk3QP/+/TEMg127djV5zsyZM6moqIi+du7c2Xpf4gjY874AoDhxAACZ3kwu6XUJANUfmb3fcSNGKOkWERE5xtl85sg6b1OJt1tzvEVEpOVaPfF2Op0MGzaMRYsWNdi+aNEiRo8e3eQ5Y8aMYc+ePVRXV0e3bdy4EavVSufOnZs8x+VykZiY2OB1LOhY/CkAtdmNq5TX1C+pFn/G2KMak4iIiBwawzBYuzWfpHCY+Dqj0X6LV1XNRUSk5dpkHe8ZM2bw9NNP8+yzz7J+/XruuOMOcnNzmTp1KmD2Vl933XXR46+++mrS0tK48cYbWbduHR999BF33303N910U5PDzI9VdXkb6BzaQcCw8Yj1fa57+zq2lG8BIFxVhW+1ub533Fgl3iIiIscyfyjCyngfFTZbdO3u/anHW0REDkWbzPGePHkyJSUlzJo1i7y8PAYOHMjChQvp2rUrAHl5eeTm5kaPj4+PZ9GiRdx2220MHz6ctLQ0rrzySh588MG2CK/N7Fr+b3oBy+wnsaVqM0aVQbIrGYCaZZ9COIyze3eczfTii4iIyLGhqi5Era35Od4WJd4iInII2qy42rRp05g2bVqT+xYsWNBoW79+/RoNTz/eODe/DcCqDidj8DE5CTmkedIAqF76EQBxY0+PWXwiIiLSMjX+EEFrGEvEwFu/iEqtEzwBsHg8zRaMFRERaUqbDDU/IVUV0LnmawAKO2YAcErGKdHdvk+XAxCvYeYiIiLHvGp/iKA10mCY+Q6zedca3iIicsiUeLeWjW9jxaA8ZSAlNrOi+97EO1xeTnD3bgA8p5zS7CVERETk2FDjD+HfL/EOuGwUpJi93Eq8RUTkUCnxbi07VwCQOHAS60rMnu+9iXfd+vUAOHJysB0j1ddFRESkeTWBEHVWIzq/u85jo7B+1VOLR/O7RUTk0CjxbgWGYWBUmOuIb41LoipYhcfuoXdKbwDq1q0DwH3SSTGLUURERFouJ8VLwEZ0KTGfx0px0t4eby0lJiIih6bNiqudSD7dWkLO9k3kAOH4DMbnjMdpdWK3mn+9devMHm8l3iIiIseH3lkJ5IRDFPjMPooSRx1ru9mIpCQSP25cjKMTEZHjjRLvVvDcx9t4LFIEFujbcQSPDfxBg/3q8RYRETnOGAa/Kipic0ECQRKocVs4f9QNJNxwJRmJXWIdnYiIHGeUeB+h3BIfX3y7GbcriIEFS2LHBvsjNTUEtm8HwH1S/xhEKCIiIoeqsKyCTMDjNwgCNW64behtuGyuWIcmIiLHIc3xPkKvr9lNB4oBqIvPZE9dCYZhUPvNNxQ9+hg1K1aAYWDPzsaelhbjaEVERKQlFny0AQC73/ypVO2G0trSWIYkIiLHMfV4H6HcUh8dLSUAfJ6cwa2vnMPwrOHc/w/wLV+OxWsWYHH3V2+3iIjI8cJfW8NZOR257muD04Bqt4WvS74m05uJzWqLdXgiInKcUY/3ESqo8kcT7zX163rmJOTg32A+KTd8PkDzu0VERI4ntYEKCu12jKD5U6nGDTOWzKAqUBXjyERE5HikxPsIFVTU7Uu8LSEAhrl6Ey4rA4j2eHtOGRSbAEVERNrIvHnz6N69O263m2HDhrF06dIDHu/3+7nvvvvo2rUrLpeLnj178uyzzx6laA9Nrb8cgPhaczmxmvqlu112zfEWEZFDp6HmR2hwTjID6qoI+uHrUAUAA6qSCAKOTp3o/OfHqF2zhrjTT49toCIiIq3o5ZdfZvr06cybN48xY8bwl7/8hUmTJrFu3Tq6dGm66veVV15JQUEBzzzzDL169aKwsJBQKHSUI2+ZumAluCChzvxcvTfxVnE1ERE5DEq8j9AfLh8ET9fyTaGTOiNEojOR1PwaCgBnr564+/fX/G4REWl35syZw5QpU7j55psBmDt3Lu+++y7z589n9uzZjY5/5513+PDDD9m6dSupqakAdOvW7WiGfEj8oUoA4usT7xq3BbfNjdWiwYIiInLo1Hq0hopdrHabT8AHZw4msGUrAK6evWIZlYiISJsIBAKsWrWKiRMnNtg+ceJEli1b1uQ5b7zxBsOHD+ehhx6iU6dO9OnTh7vuuova2tqjEfIhC4SqAfBGE28NMxcRkcOnHu8jEApHsEZCWKvyWZNhPr0fnDEY/xbzR4erZ89YhiciItImiouLCYfDZGVlNdielZVFfn5+k+ds3bqVjz/+GLfbzauvvkpxcTHTpk2jtLS02Xnefr8fv98f/VxZWdl6X+IgBnR2sNZn4A6Yn6s94LW5j9r9RUSkfVGP9xFY+HU+Z97/EmBwiS/Atf1/yOhOo/fr8e4R2wBFRETakMViafDZMIxG2/aKRCJYLBZefPFFTjvtNM477zzmzJnDggULmu31nj17NklJSdFXTk5Oq3+H5ozpksTAymD0c40bCnwFR+3+IiLSvijxPgKFlXVkRIoBON2Zzj2n/Yx+jhxChYUAONXjLSIi7VB6ejo2m61R73ZhYWGjXvC9OnToQKdOnUhKSopu69+/P4ZhsGvXribPmTlzJhUVFdHXzp07W+9LHMQZ8d15LrcIAEtcHH3S+zNt8LSjdn8REWlflHgfgfyKOjpazMSbpM4A+LdsAcCelYUtISFWoYmIiLQZp9PJsGHDWLRoUYPtixYtYvTo0U2eM2bMGPbs2UN1dXV028aNG7FarXTu3LnJc1wuF4mJiQ1eR0MwHKG4vAJ/hQMAZ8cO/OvCf/F/p/zfUbm/iIi0P0q8j0B+ZR2dLCUs87j5PC4BX9BHoD7x1vxuERFpz2bMmMHTTz/Ns88+y/r167njjjvIzc1l6tSpgNlbfd1110WPv/rqq0lLS+PGG29k3bp1fPTRR9x9993cdNNNeDyeWH2NRurWreODN5cy95211Babibdn8JAYRyUiIsc7FVc7AoWVfs61bufRlCS+8X3NH3YuYdhmM/HWMHMREWnPJk+eTElJCbNmzSIvL4+BAweycOFCunbtCkBeXh65ubnR4+Pj41m0aBG33XYbw4cPJy0tjSuvvJIHH3wwVl+hkUggwI4bbiS7ppad13RgZVU83QHPECXeIiJyZJR4H4H8yjr62zbzrdMLmEuJ1a17GQBXby0lJiIi7du0adOYNq3pec8LFixotK1fv36NhqcfSyKVlUQqK3EC/XPL6ZRvForzDBkc07hEROT4p6Hmh8kwDIzKPZS6qwlbLGR6Msh2pFH75ZcAeIcOjXGEIiIicihCVfvmn09YVY0zDCGvDWe3brELSkRE2gUl3ofJH4pwfedCvnS5ABicOQT/+vUYfj+25GQNNRcRETnOFBaVR98nVkUACHRNaHaJNBERkZZS4n2Y3A4bN3cv5iuXE4BBGYPwrVoFgGfYMDXSIiIix5m8PSWNthndU2MQiYiItDdKvI+AsfMz1tb3eJuJ9xeAhpmLiIgcj4qKyhptc/Rsel1yERGRQ6HE+zD563zkFX5Nsd2GzWKlb3Ifaut7vL3Dh8U4OhERETlUYzuZxVKtCebyZgEbeHp2jGVIIiLSTqiq+WH6x5sLuTpQy4L8agqveBzbjj2EKyqweDy4Tzop1uGJiIjIITIqSgHwJpTxtzMSKXIGuCc+JcZRiYhIe6DE+3DtWokDyI4fyLAe51H2D3MZMc8pp2BxOGIbm4iIiByySGkeAFZ7hJkdaqG6ADzpMY5KRETaAw01PwyRiEFc+UYAnJ0HA1D94YcAeIcPj1VYIiIicpgiEYOlK78FwGo3zKQbwO6OYVQiItJeKPE+DFuLa+hs5DIrLYXFCUFqC/OpXroUgMTzJsU4OhERETlUeypqqSkpAsDqMPbtsLtiFJGIiLQnGmp+GL7aWUYXVwH/SkwmPu8DzsztB6EQ7kGDcPXoEevwRERE5BDtKPGRFKox33tsXN+5IxN8Pn7m8MQ4MhERaQ/U430YtmzbyjZ3GIABaQOpfO11AJIuuTiWYYmIiMhh2lZcQ3yoFoBit5XdDjvlNpt6vEVEpFUo8T4MlTu/Zr3TCcBIX0f8336LxeEg6bzzYhyZiIiIHI4dJTV4Q3WAmXgDdAkGNcdbRERahRLvw3B+dgXrXWbiPXBNGQDxEyZgS06OYVQiIiJyuLYV+3CFggDk1Sfe3YIh9XiLiEirUOJ9GIbFF7DRYSbeyau2ApAwcWIsQxIREZEjsKOkBmd94r3LYwPU4y0iIq1Hifdh2Fb8DQGrhZxqO8aW7WC1EjdmdKzDEhERkcO0q6wWWygCQJHH/HnUVT3eIiLSSpR4H6KdpT62VGwH4Hu7UwDwnHIK9pSUGEYlIiIiR+Lr+06HkLmMWJ3DQlooTLxhqMdbRERahRLvQ/T0uyuYVF7E0h27Ob8oB4D4cWfEOCoRERE5EraaAiLB+qJqcYmc7PebO5R4i4hIK1DifYjsJd8CYHN0ILLqawDiz1DiLSIicjwzqvKJhCwAPNzvBzxWWGzuUOItIiKtQIn3IYqr3AJATU1HDJ8Pe0YGrv79YxyViIiIHK6quiAL3voYDDPxtmb32rdTc7xFRKQVKPE+BIZhYAtt5ubsTJaXmGt9ekeNxGKxxDgyEREROVw1/jAF27ZEP1s7DzDfOBPAaotRVCIi0p4o8T4EJTUBws49fOZxU1PhA8DZpWuMoxIREZEjEQxHyAqVAeB3wIUf/oQ95z4Ilz4R48hERKS9aLPEe968eXTv3h23282wYcNYunRps8cuWbIEi8XS6PXtt9+2VXiHZU95LTZHOQBZ1Q4AHB07xjAiEREROVL+UISMsJl41zqh0FdI6vAfQf8LYhyZiIi0F22SeL/88stMnz6d++67j9WrVzN27FgmTZpEbm7uAc/bsGEDeXl50Vfv3r3bIrzDlldSQcRWC0BSRRhQ4i0iInK8C4YjVNrMxLvOAXefejduFVUTEZFW1CaJ95w5c5gyZQo333wz/fv3Z+7cueTk5DB//vwDnpeZmUl2dnb0ZbMdW/Oq+jqLKLdbwTDwlJoJuKOTEm8REZHjWSAU4ZM4s3aLKy6OK/pcEeOIRESkvWn1xDsQCLBq1SomTpzYYPvEiRNZtmzZAc8dMmQIHTp04KyzzmLx4sUHPNbv91NZWdng1da6kUep1UqSD2zBMFgsOLKy2vy+IiIi0nZqQwEC5kA2MlM6xTYYERFpl1o98S4uLiYcDpP1nYQ0KyuL/Pz8Js/p0KEDTz75JK+88gr/+c9/6Nu3L2eddRYfffRRs/eZPXs2SUlJ0VdOTk6rfo8mFW+i1GYjvcL8aM/IwOJ0tv19RUREpM3U1FbiCZjvHYnJMY1FRETaJ3tbXfi7S2wZhtHsslt9+/alb9++0c+jRo1i586d/PGPf+SMM85o8pyZM2cyY8aM6OfKyso2T75Lc9dRY7WQXmkAmt8tIiLSHvRP9fFufeJtS0yNbTAiItIutXqPd3p6OjabrVHvdmFhYaNe8AMZOXIkmzZtana/y+UiMTGxwaut7dz8Fa/tzucu61hAibeIiEh7kGUY/KTIHM5mjY+LcTQiItIetXri7XQ6GTZsGIsWLWqwfdGiRYwePbrF11m9ejUdOnRo7fAOmz8UJieyGwBP0BxersJqIiIixz+brzQ61NwWp8RbRERaX5sMNZ8xYwbXXnstw4cPZ9SoUTz55JPk5uYydepUwBwmvnv3bp5//nkA5s6dS7du3RgwYACBQIC//e1vvPLKK7zyyittEd5hKcrPo7OlGgBrpR9Qj7eIiEh7sHXHdrxBsy/C4vXGOBoREWmP2iTxnjx5MiUlJcyaNYu8vDwGDhzIwoUL6dq1KwB5eXkN1vQOBALcdddd7N69G4/Hw4ABA3jrrbc477zz2iK8w1K+ez1+h53fp2Zy87KvSUSJt4iISHvwZeE3eG0uuqAebxERaRttVlxt2rRpTJs2rcl9CxYsaPD5nnvu4Z577mmrUFpFXdF2Su12lnvt/F9ROQD2Y2govIiIiByefP9uPIaDLhhYlXiLiEgbaPU53u1VqKqIEpsNj9/AUxsBwNFRa32KiIgc74LBfcuJKfEWEZG2oMS7hSI1JZTZrKRXmp+tSUnYVPlURETkuBcIV+MOmEuFWjXHW0RE2oAS7xbq4a2l1GYjvUJreIuIiLQnwUgNbvV4i4hIG1Li3ULZ9mpKrTYyzWU+lXiLiIi0E8FInYaai4hIm1Li3VK+UkptVrLLzB5vZ05OjAMSERGR1hDAvy/x1lBzERFpA0q8W6i2vIAaq5WsMvOzs2uX2AYkIiIiRy4UIGCENdRcRETalBLvFvKVF/BCXgHD67IBcOQo8RYRETnu+Yq5t7Acu7lgCbbExNjGIyIi7ZIS7xYIhkIkGVUYBlBYAoCzi4aai4iIHPdqikmoM6eRYbNh0VBzERFpA0q8W6C8pAi7JUKo1gqBANjtKq4mIiLSHtQUEQ6YP4eM+AQsFkuMAxIRkfZIiXcLVJbmk2+z8XtHJmBWNLfY7TGOSkRERI6Yr4TXnPEA1LrcMQ5GRETaKyXeLVBTVkC+3UZRrZlsq6K5iIhIO1FTzKc2c3h50OuKcTAiItJeqdu2BeoqCqmy2aJLiTk0v1tERKR9qCnCGrAABpH4hFhHIyIi7ZR6vFsgWFlIvt1GVrn52dmla0zjERERkdYRqSnC4TfndVviVNFcRETahhLvFujurWOP3U5WfY+3KpqLiIi0D3U1hcTVme8tickxjUVERNovJd4t0NHhI89mJ7vM/OzQHG8REZF2ocZXTFz9cmI2Jd4iItJGlHi3hK+Y8pCNOL/5UcXVRERE2gdfbUm0fbcmaKi5iIi0DSXeLVBVmo+7ov6vKj0Vq8cT24BERESkVdSEfNGh5p27ZMY2GBERabdU1bwF9uzZxeyt5ewhBU+37rEOR0RERFpJT38Aa0UEAyeelORYhyMiIu2UerxbICFSgb/cfEbh7tMnxtGIiIhIa3FFQjjrq5pbEzXUXERE2oYS74MIhSOkGJXUVTgAcCnxFhERaT/CQcIB8+eQz6GpZCIi0jY01PwgSivKWRtnI6HKRQJKvEVERNqTLx0WrCErdqDOHRfrcEREpJ1Sj/dBVJbks8VwklBtDkNz9eoV44hERESODfPmzaN79+643W6GDRvG0qVLmz12yZIlWCyWRq9vv/32KEb8HYbBV3YH9qDZxjuTk2IXi4iItGtKvA+ipiyfugpzYEBdWgI2zf8SERHh5ZdfZvr06dx3332sXr2asWPHMmnSJHJzcw943oYNG8jLy4u+evfufZQibkI4iL8+6QZwJauNFxGRtqHE+yD8laVYyszEO9y9U4yjEREROTbMmTOHKVOmcPPNN9O/f3/mzp1LTk4O8+fPP+B5mZmZZGdnR182m+0oRdyESJBQwLy/z2HD5XbGLhYREWnXlHgfRCdPEG+p+dfk6N0zxtGIiIjEXiAQYNWqVUycOLHB9okTJ7Js2bIDnjtkyBA6dOjAWWedxeLFiw94rN/vp7KyssGrVYWDhOp7vGucDhw2/SwSEZG2oRbmIDq4A6SVmI1yYv+TYxyNiIhI7BUXFxMOh8nKymqwPSsri/z8/CbP6dChA08++SSvvPIK//nPf+jbty9nnXUWH330UbP3mT17NklJSdFXTk5Oq34PImEiQfOnULXLid1qOcgJIiIih0dVzQ+ipKaATsXm+4yBw2IbjIiIyDHEYmmYqBqG0WjbXn379qVv377Rz6NGjWLnzp388Y9/5IwzzmjynJkzZzJjxozo58rKytZNviNBDL+ZeNc4Xc3GLiIicqTU430QO7ZtI74Owhbw9tJSYiIiIunp6dhstka924WFhY16wQ9k5MiRbNq0qdn9LpeLxMTEBq9WFQ5iCZjJdnantNa9toiIyH6UeB+Eb20BANYUL1aniq6IiIg4nU6GDRvGokWLGmxftGgRo0ePbvF1Vq9eTYcOHVo7vJaLBDm/1AdATqcesYtDRETaPQ01PwibrwYAi9cd40hERESOHTNmzODaa69l+PDhjBo1iieffJLc3FymTp0KmMPEd+/ezfPPPw/A3Llz6datGwMGDCAQCPC3v/2NV155hVdeeSV2XyIcIsEPJYAnJT12cYiISLunxPsg7HV7E29PjCMRERE5dkyePJmSkhJmzZpFXl4eAwcOZOHChXTt2hWAvLy8Bmt6BwIB7rrrLnbv3o3H42HAgAG89dZbnHfeebH6ChAJEQ6Yg/++rjRo+SB5ERGRQ6PE+yByqWYgUOQK0S/WwYiIiBxDpk2bxrRp05rct2DBggaf77nnHu65556jENUhiATZYjhJAz4pqOSsWMcjIiLtluZ4H4BhGNSGQwDUuFXpVEREpF0Jh8iPOACo86ovQkRE2o4S7wOo8oewBcMA2BLiYhyNiIiItKpIEE+d+TbgiY9tLCIi0q4p8T6A8pog1pABgCMhKcbRiIiISKuKhPD6zbchtxJvERFpO0q8D8DrsmEPmu+dickxjUVERERalxEK4A6Y7yMejWwTEZG2o8T7ANI9Vqz1DbIzOS22wYiIiEirCkcC2MwZZdicrtgGIyIi7ZoS7wPxV2EPmkXVPCnZMQ5GREREWlMo5McWMd/b7Eq8RUSk7SjxPoCikiJc9T3e3uTM2AYjIiIirSoUrsNen3jfNK5vbIMREZF2TYn3Aby3aiO9a8zlxDIyusY4GhEREWlNbiw4wmYR1Z4dNKVMRETajhLvAwjUVBCuH2puj1e1UxERkfbEFo5gMcx23uZwxjgaERFpz5R4H0DQV0GkPvG2KvEWERFpX4L+6NuVuypjGIiIiLR3bZZ4z5s3j+7du+N2uxk2bBhLly5t0XmffPIJdrudwYMHt1VoLVZStw0jbP4VKfEWERFpXypqK6LvF28ujWEkIiLS3rVJ4v3yyy8zffp07rvvPlavXs3YsWOZNGkSubm5BzyvoqKC6667jrPOOqstwjpktbWF0ffWOK3vKSIi0p6U7Jd4O1waai4iIm2nTRLvOXPmMGXKFG6++Wb69+/P3LlzycnJYf78+Qc875ZbbuHqq69m1KhRbRHWoaszG+SQDaxONcgiIiLtSSi0b6i53WmPYSQiItLetXriHQgEWLVqFRMnTmywfeLEiSxbtqzZ85577jm2bNnCr3/96xbdx+/3U1lZ2eDV2qz+agCCLk2FFxERaW/CfnPN0AjgdDpiG4yIiLRrrZ5RFhcXEw6HycrKarA9KyuL/Pz8Js/ZtGkT9957Ly+++CJ2e8ueOM+ePZukpKToKycn54hj/650ZxiAkMvW6tcWERGR2Nrb4x22gdOmh+wiItJ22qyVsVgsDT4bhtFoG0A4HObqq6/mgQceoE+fPi2+/syZM6moqIi+du7cecQxf1e8xUy8Ix4NPxMREWlvQgGzxztsBaddibeIiLSdVs8o09PTsdlsjXq3CwsLG/WCA1RVVbFy5UpWr17NrbfeCkAkEsEwDOx2O++99x5nnnlmo/NcLhcul6u1w28gUFtnvvFofreIiEh7E67v8Y5Y1eMtIiJtq9VbGafTybBhw1i0aFGD7YsWLWL06NGNjk9MTGTt2rWsWbMm+po6dSp9+/ZlzZo1jBgxorVDbJG6YBijNgiAxeuOSQwiIiLSdsJBs52PWC2M75sZ42hERKQ9a5Mx1DNmzODaa69l+PDhjBo1iieffJLc3FymTp0KmMPEd+/ezfPPP4/VamXgwIENzs/MzMTtdjfafjSty6vkjDyzQe6Y2jlmcYiIiEjb6GFxUwLEORxkJ+khu4iItJ02SbwnT55MSUkJs2bNIi8vj4EDB7Jw4UK6du0KQF5e3kHX9I61QChCUihAADv2hKRYhyMiIiKtLCFsoQRw2FTLRURE2labtTTTpk1j2rRpTe5bsGDBAc+9//77uf/++1s/qEMQDEdwBMMEsGNNSIxpLCIiItL6jPqh5kEgr6KWDkme2AYkIiLtliqJNCMQDLPCYa7p6Y9TQywiItLe5PmrACgPh9haVBPjaEREpD1T4t2MQF0FZRFz/W5rUmqMoxEREZHWtjlgJt61jrCWExMRkTalVqYZvpp8POYqI3hTsmMbjIiIiLS6SCgEQNhqwaHlxEREpA2plWmGr64Qb8AAwK453iIiIu1OJBQ2/7RatI63iIi0KZXxbEa6N4ilvsfbGh8X22BERESk1UXC+3q8XQ4l3iIi0nbUyjSjY5IFT8B8b4uPj20wIiIi0uqMcAQwE2+3wxbjaEREpD1T4t2MQNAXneNtVeItIiLS7oTD9UPNLRZcKq4mIiJtSK1MM6p8VdEebyXeIiIi7c/eHm+Px0mi2xHjaEREpD3THO9mVG910sFsj7HGaY63iLRcOBwmGAzGOgw5ChwOBzabhigfr04O2QkBXRLStJyYiBwStfUnjtZq65V4N8NSUxV9b/V6YxiJiBwvDMMgPz+f8vLyWIciR1FycjLZ2dlYLJZYhyKHKC0EBUCCSyPbRKRl1NafmFqjrVfi3QxbbY35xmHBYtVTcBE5uL0NcWZmJl6vV4lYO2cYBj6fj8LCQgA6dOgQ44jkUO1dTqy0LkznGMciIscHtfUnltZs65V4N2N33U66A3UO/Z9JRA4uHA5HG+K0tLRYhyNHicfjAaCwsJDMzEwNOz/O7AoHcAEbKqoYFOtgROSYp7b+xNRabb26cptRFSoHwG83YhuIiBwX9s7z8mpqygln77+55vodf77FXMfb56yNcSQicjxQW3/iao22Xol3M4z6v9SITT3eItJyGnJ24tG/+fHLCJsP1w2rRiqISMvpv/snntb4N1fi3QxLyFxLLGLTX5GIiEi7FKlPvDVFQERE2piyymbE1Q8xN+x6oiUisr8lS5ZgsVgOWNF1wYIFJCcnH/RaFouF1157rdViEzkk9Yl3RIm3iEgDautbnxLvZiTWl52zONUYi0j79cQTT5CQkEAoFIpuq66uxuFwMHbs2AbHLl26FIvFQseOHcnLyyMpKanF97n//vsZPHhwq8RcV1fHDTfcwMknn4zdbueSSy5plevKCcgsao5hc8Q2DhGRNqS2/tigxLsZkWD9/zA11FxE2rEJEyZQXV3NypUro9uWLl1KdnY2K1aswOfzRbcvWbKEjh070qdPn5iuWx0Oh/F4PNx+++2cffbZMYlB2onoUHMt8iIi7Zfa+mODsspmhAJmcTXDrh5vEWm/+vbtS8eOHVmyZEl025IlS7j44ovp2bMny5Yta7B9woQJTQ4/W7BgAV26dMHr9XLppZdSUlLSYN8DDzzAl19+icViwWKxsGDBguj+4uJiLr30UrxeL7179+aNN944YMxxcXHMnz+fH/3oR2RnZx/x34GcuCz1C5d0TI+PbSAiIm1Ibf2xQYl3M3oWmsPOUm0JMY5ERI53vkCo2VddMNyqxx6O8ePHs3jx4ujnxYsXM378eMaNGxfdHggE+PTTT5kwYUKj8z/77DNuuukmpk2bxpo1a5gwYQIPPvhgdP/kyZO58847GTBgAHl5eeTl5TF58uTo/gceeIArr7ySr776ivPOO49rrrmG0tLSw/ouIofipDrz/zP9MrvGOBIROd4dzbb+cNp7tfWxp7FVzXDUz4GwOZwxjkREjncn/erdZvdN6JvBczeeFv087DfvU/udRnevEd1TefmWUdHPp/9hMaU1gQbHbP/9+Ycc3/jx47njjjsIhULU1tayevVqzjjjDMLhMI8++igAy5cvp7a2lgkTJpCbm9vg/EceeYRzzjmHe++9F4A+ffqwbNky3nnnHQA8Hg/x8fHY7fYmn1rfcMMNXHXVVQD87ne/47HHHuPzzz/n3HPPPeTvInIo0oNhKnCQFJcW61BE5Dh3NNt6OPT2Xm197KnHuxm2sJl4W5wquCIi7duECROoqalhxYoVLF26lD59+pCZmcm4ceNYsWIFNTU1LFmyhC5dutCjR49G569fv55Ro0Y12PbdzwcyaNCg6Pu4uDgSEhIoLCwEYMCAAcTHxxMfH8+kSZMO8xuKNMEwMMIRAIrqjBgHIyLSttTWx556vJuxzV7LQKDCdnhDN0VE9lo365xm91m/U7Rk1S+bLyDy3WM//lnjoWCHo1evXnTu3JnFixdTVlbGuHHjAMjOzqZ79+588sknLF68mDPPPLPJ8w3jyJIWh6PhA06LxUIkYiZECxcuJBg0a254PJ4juo9IA5EwxRYbTuCLgjL6xzoeETmuqa0/MLX1SrybVWIxE+4aWyTGkYjI8c7rbPl/atvq2IPZW0ilrKyMu+++O7p93LhxvPvuuyxfvpwbb7yxyXNPOukkli9f3mDbdz87nU7C4aaH1R1I166aeyttJBJkk93JAKDWVh3raETkOKe2Xm39wSjxboalfokRq1NzvEWk/ZswYQI/+clPCAaD0afgYDbG//d//0ddXV2TxVYAbr/9dkaPHs1DDz3EJZdcwnvvvRed87VXt27d2LZtG2vWrKFz584kJCTgcrkOO95169YRCAQoLS2lqqqKNWvWALTa+qFyAggHsdQ/W7fYD/9/iyIixwu19bGlOd7NsIbNxNuixFtETgATJkygtraWXr16kZWVFd0+btw4qqqq6NmzJzk5OU2eO3LkSJ5++mkee+wxBg8ezHvvvccvfvGLBsdcdtllnHvuuUyYMIGMjAxeeumlI4r3vPPOY8iQIbz55pssWbKEIUOGMGTIkCO6ppxgIiGs9Ym3zeWObSwiIkeB2vrYshhHOmD/GFFZWUlSUhIVFRUkJiYe8fWe+8FJjFxjUH7BUEb98cVWiFBE2rO6ujq2bdtG9+7dcbv1I/5E0ty/fWu3S9LKf6fVhbz2/XH0zYUV1/+Q62be1zpBiki7pbb+xHWgf/uWtk3q8W7G3qndbq83toGIiIhI6wsHoz3edvV4i4hIG1Pi3QxLfV0ADT8TERFphyJBrPVj/r5bbVdERKS1KfFugmEY0TneViXeIiIi7U8kHH3I3jUzOaahiIhI+6fEuwkVtUFOrTbXkstI6BDjaERERKTVhYN0DJqZd/fM7BgHIyIi7Z0S7yYEwhGcYXPilzMuIcbRiIiIHJvmzZsXLTQzbNgwli5d2qLzPvnkE+x2e2yXhIkESQqZbb3Xo+J3IiLStpR4NyEQDEfX9rS6VFxNRETku15++WWmT5/Offfdx+rVqxk7diyTJk0iNzf3gOdVVFRw3XXXcdZZZx2lSJsRDmJELADUhGMbioiItH9KvJsQDAbItdoBCNj1VyQiIvJdc+bMYcqUKdx8883079+fuXPnkpOTw/z58w943i233MLVV1/NqFGjjlKkTYuEA9QaZuK9p8of01hERKT9U1bZBH9tNWXYAAi7nDGORkRE5NgSCARYtWoVEydObLB94sSJLFu2rNnznnvuObZs2cKvf/3rFt3H7/dTWVnZ4NVaAqE6yqxmW29VUy8iIm1MiXcTfHVVOOqHnTncmvclIiKyv+LiYsLhMFlZWQ22Z2VlkZ+f3+Q5mzZt4t577+XFF1/Ebre36D6zZ88mKSkp+srJyTni2PcKheqw1bf1Hren1a4rIiLSFCXeTagLVGPfm3h742MbjIjIMWbJkiVYLBbKy8ubPWbBggUkJycf9FoWi4XXXnut1WKTo8tisTT4bBhGo20A4XCYq6++mgceeIA+ffq0+PozZ86koqIi+tq5c+cRx7xXKOzHVl/Pxe1R4i0isj+19a1PiXcTPLbaaOJt1zreItKOPfHEEyQkJBAKhaLbqqurcTgcjB07tsGxS5cuxWKx0LFjR/Ly8khKSmrxfe6///5WrWC9du1axo0bh8fjoVOnTsyaNQvDMJo9fu8PiKZeK1asiB7X1P4nnnii1eJuL9LT07HZbI16twsLCxv1ggNUVVWxcuVKbr31Vux2O3a7nVmzZvHll19it9v54IMPmryPy+UiMTGxwau11AZqsdcn3l4l3iLSjqmtPzba+paN9TrBZMbtq3BqcWjil4i0XxMmTKC6upqVK1cycuRIwGx0s7OzWbFiBT6fD6/XXN1hyZIldOzY8ZB6LNtCZWUl3/ve95gwYQIrVqxg48aN3HDDDcTFxXHnnXc2ec7o0aPJy8trsO2Xv/wl77//PsOHD2+w/bnnnuPcc8+Nfj6UHx0nCqfTybBhw1i0aBGXXnppdPuiRYu4+OKLGx2fmJjI2rVrG2ybN28eH3zwAf/+97/p3r17m8f8XXXBfUPNnS7HUb+/iMjRorb+2Gjr1ePdhEDQh6P+gZDFqcZYRNqvvn370rFjR5YsWRLdtmTJEi6++GJ69uzZoFDWkiVLmDBhQpPDzxYsWECXLl3wer1ceumllJSUNNj3wAMP8OWXX0afLC9YsCC6v7i4mEsvvRSv10vv3r154403Dhjziy++SF1dHQsWLGDgwIF8//vf5+c//zlz5sxp9km40+kkOzs7+kpLS+ONN97gpptuajQ0Ojk5ucGxHvWGNmnGjBk8/fTTPPvss6xfv5477riD3Nxcpk6dCpjDxK+77joArFYrAwcObPDKzMzE7XYzcOBA4uLijnr8dYF9Q82dKqQqIu2Y2vpjo61X4t2EukB1dPiZ1anGWEQOk2FAoObovw4wDKsp48ePZ/HixdHPixcvZvz48YwbNy66PRAI8OmnnzJhwoRG53/22WfcdNNNTJs2jTVr1jBhwgQefPDB6P7Jkydz5513MmDAAPLy8sjLy2Py5MnR/Q888ABXXnklX331Feeddx7XXHMNpaWlzcb76aefMm7cOFwuV3TbOeecw549e9i+fXuLvvMbb7xBcXExN9xwQ6N9t956K+np6Zx66qk88cQTRCKRFl3zRDN58mTmzp3LrFmzGDx4MB999BELFy6ka9euAOTl5R10Te9YshCMtvU2PWQXkcMVq7b+ENt7tfUNxaKt11DzJhTuNsgMGoAFixJvETlcQR/8ruPRv+/P94Cz5T2I48eP54477iAUClFbW8vq1as544wzCIfDPProowAsX76c2tpaJkyY0CiZeuSRRzjnnHO49957AejTpw/Lli3jnXfeAcDj8RAfH4/dbic7O7vR/W+44QauuuoqAH73u9/x2GOP8fnnnzcYAra//Px8unXr1mDb3nnF+fn5LRq2/Mwzz3DOOec0qpL9m9/8hrPOOguPx8P//vc/7rzzToqLi/nFL35x0GueiKZNm8a0adOa3Ld/T0dT7r//fu6///7WD6qFsm0u9g5ItLSwyrqISCOxauvhkNp7tfX7xKqtb7Me73nz5tG9e3fcbjfDhg1j6dKlzR778ccfM2bMGNLS0vB4PPTr148//elPbRXaQVmCIYiYwxEsDj0FF5H2bcKECdTU1LBixQqWLl1Knz59yMzMZNy4caxYsYKamhqWLFlCly5d6NGjR6Pz169fz6hRoxps++7nAxk0aFD0fVxcHAkJCRQWFgIwYMAA4uPjiY+PZ9KkSdHjmqqm3dT2puzatYt3332XKVOmNNr3i1/8glGjRjF48GDuvPNOZs2axf/7f/+vxd9Fjh+Jkf3+t2JXWy8i7Zva+n1i1da3ySPel19+menTpzNv3jzGjBnDX/7yFyZNmsS6devo0qVLo+Pj4uK49dZbGTRoEHFxcXz88cfccsstxMXF8eMf/7gtQjwgI+CLvlePt4gcNofXfBodi/segl69etG5c2cWL15MWVkZ48aNAyA7O5vu3bvzySefsHjxYs4888wmzz9QhdEWhfudB5wWiyU65GvhwoUEg0GA6Pyr7OzsJqtpA01W1P6u5557jrS0NC666KKDHjty5EgqKyspKCho0bXl+OGr2a+td6jHW0QOU6za+r33biG19c07Wm19m7Q0c+bMYcqUKdx8880AzJ07l3fffZf58+cze/bsRscPGTKEIUOGRD9369aN//znPyxdujQmiXd+zW4y698r8RaRw2axHNKQ71jaW0ilrKyMu+++O7p93LhxvPvuuyxfvpwbb7yxyXNPOukkli9f3mDbdz87nU7C4fAhx7V3vvD+Ro0axc9//nMCgQDO+v9Gv/fee3Ts2LHRsLTvMgyD5557juuuu67Rj4CmrF69Grfb3aJ1SuX4sq2kNPojyGKzxTQWETmOqa2PUlt/YK0+1DwQCLBq1SomTpzYYPvEiRMbVMw7kNWrV7Ns2bLok5im+P1+KisrG7xaS1FdYfS9hpqLyIlgwoQJfPzxx6xZs6bBf3vHjRvHU089RV1dXZPFVgBuv/123nnnHR566CE2btzIn//85+icr726devGtm3bWLNmDcXFxfj9/sOO9eqrr8blcnHDDTfw9ddf8+qrr/K73/2OGTNmRIefff755/Tr14/du3c3OPeDDz5g27ZtTQ49e/PNN3nqqaf4+uuv2bJlC08//TT33XcfP/7xjxsUd5H2YWttwb4PSrxF5ASgtj62bX2rJ97FxcWEw+FG3fRZWVmNhgt8V+fOnXG5XAwfPpyf/OQn0R7zpsyePZukpKTo67uT5o+EEagFzGneKrgiIieCCRMmUFtbS69evRr893vcuHFUVVXRs2fPZv87O3LkSJ5++mkee+wxBg8ezHvvvdeoQMlll13Gueeey4QJE8jIyOCll1467FiTkpJYtGgRu3btYvjw4UybNo0ZM2YwY8aM6DE+n48NGzZEh67t9cwzzzB69Gj69+/f6LoOh4N58+YxatQoBg0axCOPPMKsWbN4+OGHDztWOXaFA+YPwpC1ZfMFRUSOd2rrY9vWW4wjHbD/HXv27KFTp04sW7aswYT73/72t7zwwgt8++23zZ67bds2qqurWb58Offeey9//vOfo9Xvvsvv9zd4ilJZWUlOTg4VFRUkJiYe0Xd45JGrmTh/NUE7DPp6/RFdS0RODHV1dWzbti1aVFJOHM3921dWVpKUlNQq7ZKYWvPv9J+PX8vJj63Eb4fBautFpAXU1p+4DvRv39K2qdW7c9PT07HZbE1Ohj/YZPW9ZeFPPvlkCgoKuP/++5tNvF0uV5sNB/BYzLkJYZuegIuIiLRH4aD58D6iUeYiInIUtPpQc6fTybBhw1i0aFGD7YsWLWL06NEtvo5hGEc0L+BIpNfn84ZdibeIiEh7FA6HAIhY1daLiEjba5MJzDNmzODaa69l+PDhjBo1iieffJLc3FymTp0KwMyZM9m9ezfPP/88AI8//jhdunShX79+gLmu9x//+Eduu+22tgjvoMLBAAAR9XiLiIi0S5GQOScw0updECIiIo21SeI9efJkSkpKmDVrFnl5eQwcOJCFCxdGS8Xn5eWRm5sbPT4SiTBz5ky2bduG3W6nZ8+e/P73v+eWW25pi/AOKhyoT7zV4y0iItIuxde38YYesouIyFHQZiW7p02bxrRp05rct2DBggafb7vttpj1bjclq8D8a3FbtYa3iIhIe3SSIw4D8DjU1ouISNvTAKsmeANmcTWbTWt4i4iItEddDLONj7Mr8RYRkbanxLsJ1vp5X9hV6lRERKQ9qvHVARCx6qeQiIi0PbU2TcilCgCfvVWXOBcREZFjxIbiMgBqIpEYRyIiIicCJd5NyLXWAlBjV2MsIiLSHq2zmg/Zy6yxWbpUREROLEq8m2AN1SfcNg01FxH5riVLlmCxWCgvL2/2mAULFpCcnHzQa1ksFl577bVWi02kpYyw2dYbNv0UEhH5LrX1rU+tTRMsYXOIueFos6LvIiLHhCeeeIKEhARCoVB0W3V1NQ6Hg7FjxzY4dunSpVgsFjp27EheXh5JSUktvs/999/P4MGDWyts1q5dy7hx4/B4PHTq1IlZs2ZhGAeeHtStWzcsFkuD17333ttqMclxJqLEW0RODGrrj422XpllE6yY/6BWp6qai0j7NmHCBKqrq1m5ciUjR44EzEY3OzubFStW4PP58Hq9gPn0u2PHjvTp0yeWIVNZ+f/bu/+oqOr8f+DPAWaG4YeYvxgmBBEkNvBHQJvY6jBuIlSrqSdJPSWpbK2ZP6BOapY/1rXWzI5ppmVB7fZR2vPVTnvyqLSBYYiyCqXmqtUYphBhKsiv4cf7+wdwc5gBRmC4M/B8nHPPYd73fWde931n5sX73ve8bzkmTZoEg8GA/Px8nD9/HklJSfD09ERqamq7265btw7JycnSYy8vL3uHS46q+Yo3XHgfbyLq3ZjrHSPX8zSvFarmEynuGnd5AyEisrO77roLOp0O2dnZUll2djamTp2K4OBg5ObmmpUbDAarw8/S09MREBAADw8PTJs2DVevXjVbt3btWnz99dfS2ef09HRpfVlZGaZNmwYPDw+MGDECn376absxf/TRR6ipqUF6ejoiIiIwffp0rFy5Eps3b+7wTLi3tze0Wq20OEoyJhmIlive/FkZEfVuzPWOkevZ8bamnle8iaj7VNVVtbnUNtTaXLemvqbDup0RGxuLrKws6XFWVhZiY2Oh1+ulcpPJhKNHj8JgMFhsf+zYMcybNw8LFy5EYWEhDAYD1q9fL61PTExEamoqwsPDUVxcjOLiYiQmJkrr165di5kzZ+Kbb77Bgw8+iDlz5uDXX39tM96jR49Cr9dDrVZLZZMnT8aVK1dw8eLFdvf173//OwYOHIgxY8bgb3/7G0wmU4ftQ72T9LMyN/4rRERd15O5vjP5nrlefhxqbk3z8DMXNa94E1HX3fd/97W5bvyd47H9ge3S49iPY1FdX221brRvNNLi06TH8f8vHtdqr5nVOTX31G3HFxsbi2XLlqG+vh7V1dUoKCjAhAkT0NDQgDfffBMAkJeXh+rqahgMBhQVFZltv2XLFkyePFn6DVVoaChyc3Nx4MABAIBGo4GXlxfc3Nyg1WotXj8pKQmzZs0CAGzYsAFbt27F8ePHER8fbzXekpISDBs2zKzM19dXWhcUFGR1uyVLliAyMhJ33HEHjh8/jhUrVsBoNGLXrl02thT1Jqrm/rYb53Mhom7Qk7keuP18z1wvf67naV4r7itvmnjAQ3mHzJEQEdmfwWBAZWUl8vPzkZOTg9DQUAwZMgR6vR75+fmorKxEdnY2AgICMHz4cIvtz549i5iYGLOy1o/bM2rUKOlvT09PeHt7o7S0FAAQHh4OLy8veHl5ISEhQaqnUJj/Lrdl2Fnr8lstW7YMer0eo0aNwoIFC7Bjxw689957ZkPlqO/4nWjqcA/0sH3iICIiZ8VcL3+u52leK3zqGlENV6jcveUOhYh6gWOzj7W5ztXF/Pel2TOz26zrojA/V3pgxoEuxdUiJCQE/v7+yMrKwrVr16DX6wEAWq0WQUFB+Oqrr5CVlYWJEyda3b6j31p1RKk0/1mPQqFAY/OM0/v370ddXR2AprPpLXGVlJSYbdOSvFvOhtuiZYKZ7777DgMHDuxc8OS0QuoVKAUwUNNf7lCIqBdgrm8fcz073la5NL8JXN01MkdCRL2Bh9JD9rodaZlI5dq1a3j++eelcr1ej4MHDyIvLw9PPvmk1W3vvvtu5OXlmZW1fqxSqdDQ0HDbcQUGBlqUxcTEYOXKlTCZTFCpVACAQ4cOQafTWQxLa09BQQEAwM/P77bjIucn6ptyvcKN/woRUdcx1zPXd4RDzVsRQuBi8z09TZxbjYj6CIPBgCNHjqCwsFA6Cw40JeN3330XNTU1VidbAYDFixfjwIED2LhxI86fP49t27ZJv/lqMWzYMBiNRhQWFqKsrAy1tbVWn8sWs2fPhlqtRlJSEk6fPo19+/Zhw4YNSElJkYafHT9+HGFhYbh8+TKApkla3njjDRQWFsJoNOLjjz/GU089hSlTpiAgIKDTsZDzqmhs+llZPe/jTUR9BHO9vJhtWqmrb8QPrk097lp2vImojzAYDKiurkZISIjZEC69Xo+KigoEBwdj6NChVrcdO3Ysdu3aha1bt2LMmDE4dOgQVq1aZVZnxowZiI+Ph8FgwODBg7F79+5Ox+rj44PMzEz89NNPiI6OxsKFC5GSkoKUlBSpTlVVFc6dOycNXVOr1cjIyEBsbCzuvvtuvPzyy0hOTu5SHOTccjyb5nG5JDp3NwAiImfDXC8vhejqgH0HUV5eDh8fH9y4cQP9+vXr9PPcrKzEvlnRiD4PuCyZj7v+8lw3RklEvVVNTQ2MRiOCgoLg7s47IvQlbR377spL9JvubNMPF8fh3kOXcG3K/Ri3Uf7ZbonI8THX913tHXtbcxOveLdSV1sDl+afJqg9HONm60RERNTN6puSvYK3EyMioh7Ajncror4arg1NvxtQatjxJiIi6pUamidXc2XHm4iI7I8d71Y83GqhbGgafa/UeMocDREREdmDovmKt4uSE7oQEZH9sePdiknpAbfmoeZKNW8nRkRE1BspGlo63iqZIyEior6AHe9WTG5KqePtxo43ERFR7yTdx5tXvImIyP7Y8W7FR+WDYZo7AQAuarXM0RAREZE9BHo05fp+HnfIHAkREfUF7Hi3onRVQi1cAQAK/u6LiIioVxruFQgAGOg1WOZIiIioL2DH2wpharoJu0LF330RERH1RqK+vukPN85qTkRE9seOtxXCZALAjjcREVFv1dLxVrDjTUREPYAdbytEXfMVbw41JyKykJ2dDYVCgevXr7dZJz09Hf379+/wuRQKBT755JNui43IVqK+OddzcjUiIgvM9d2PHW8reMWbiPqKHTt2wNvbG/Utw24B3Lx5E0qlEuPHjzerm5OTA4VCAZ1Oh+LiYvj4+Nj8OmvWrMGYMWO6K2ycOnUKer0eGo0Gd955J9atWwchRJv1L168iPnz5yMoKAgajQbBwcFYvXo1TM3f9y0UCoXFsmPHjm6LmxxIyxVvJa94E1HvxlzvGLme2cYKqePNe3sSUS9nMBhw8+ZN/Pe//8XYsWMBNCVdrVaL/Px8VFVVwcPDA0DT2W+dTofQ0FA5Q0Z5eTkmTZoEg8GA/Px8nD9/HklJSfD09ERqaqrVbf73v/+hsbERO3fuREhICE6fPo3k5GRUVlZi06ZNZnXT0tIQHx8vPb6dfzrIeYj6pnuHKlxdZY6EiMi+mOsdI9fzincroqEBaGy+t6eKw8+IqHe76667oNPpkJ2dLZVlZ2dj6tSpCA4ORm5urlm5wWCwOvwsPT0dAQEB8PDwwLRp03D16lWzdWvXrsXXX38tnVlOT0+X1peVlWHatGnw8PDAiBEj8Omnn7Yb80cffYSamhqkp6cjIiIC06dPx8qVK7F58+Y2z4THx8cjLS0NcXFxGD58OKZMmYLnnnsOe/futajbv39/aLVaadFoNB20IjkjTq5GRH0Fc71j5Hp2vFsRtwxF4BVvIuoKIQQaq6p6fGlvGJY1sbGxyMrKkh5nZWUhNjYWer1eKjeZTDh69CgMBoPF9seOHcO8efOwcOFCFBYWwmAwYP369dL6xMREpKamIjw8HMXFxSguLkZiYqK0fu3atZg5cya++eYbPPjgg5gzZw5+/fXXNuM9evQo9Ho91Gq1VDZ58mRcuXIFFy9etHm/b9y4gQEDBliUL1q0CIMGDcK9996LHTt2oLH5ZCz1Lr9NrsaT7ETUeXLl+tvN98z15uTI9TzN28qtHW8XXvEmoi4Q1dU4FxnV469718kTUDQPGbNFbGwsli1bhvr6elRXV6OgoAATJkxAQ0MD3nzzTQBAXl4eqqurYTAYUFRUZLb9li1bMHnyZCxfvhwAEBoaitzcXBw4cAAAoNFo4OXlBTc3N2i1WovXT0pKwqxZswAAGzZswNatW3H8+HGzIWC3KikpwbBhw8zKfH19pXVBQUEd7vP333+PrVu34vXXXzcr/+tf/4o//vGP0Gg0+M9//oPU1FSUlZVh1apVHT4nORdpcjX+xpuIukCuXA/cXr5nrv+NXLmeV7xbaZnRHADAWc2JqA8wGAyorKxEfn4+cnJyEBoaiiFDhkCv1yM/Px+VlZXIzs5GQEAAhg8fbrH92bNnERMTY1bW+nF7Ro0aJf3t6ekJb29vlJaWAgDCw8Ph5eUFLy8vJCQkSPUUCoXZc7Sc9W9dbs2VK1cQHx+PRx99FAsWLDBbt2rVKsTExGDMmDFITU3FunXr8Nprr9m8L+RE6ng7MSLqO5jrfyNXrme2aeW3idWUNh1UIqK2KDQa3HXyhCyveztCQkLg7++PrKwsXLt2DXq9HgCg1WoRFBSEr776CllZWZg4caLV7W93aHtrylYnORUKhTTka//+/ahrPiHa8vsrrVaLkpISs21aknfL2fC2XLlyBQaDATExMXjnnXc6jG3s2LEoLy/Hzz//3OFzk3PhfbyJqDvIletbXttWzPVt66lcz2zTiuugQRiWsadpkjUioi5QKBS3NeRbTi0TqVy7dg3PP/+8VK7X63Hw4EHk5eXhySeftLrt3Xffjby8PLOy1o9VKhUaOvG9GhgYaFEWExODlStXwmQyQdV828dDhw5Bp9NZDEu71eXLl2EwGBAVFYW0tDS4uHQ86KugoADu7u423aeUnMudb2xGY1UV1CNGyB0KETkx5vrfMNe3j0PNW3FRqaAZPRoekZFyh0JE1GMMBgOOHDmCwsJC6Sw40JSM3333XdTU1FidbAUAFi9ejAMHDmDjxo04f/48tm3bJv3mq8WwYcNgNBpRWFiIsrIy1NbWdjrW2bNnQ61WIykpCadPn8a+ffuwYcMGpKSkSCOVjh8/jrCwMFy+fBlA09nv2NhYDB06FJs2bcIvv/yCkpISs7Pp//73v/Huu+/i9OnT+P7777Fr1y68+OKL+POf/2w2uQv1Du5hYfCIjISrt7fcoRAR9QjmenlzPTveREQEg8GA6upqhISEmA2z0uv1qKioQHBwMIYOHWp127Fjx2LXrl3YunUrxowZg0OHDllMUDJjxgzEx8fDYDBg8ODB2L17d6dj9fHxQWZmJn766SdER0dj4cKFSElJQUpKilSnqqoK586dk4auHTp0CN999x2++OIL+Pv7w8/PT1paKJVKbN++HTExMRg1ahS2bNmCdevWWUzKQkRE5IyY6+XN9QrR1QH7DqK8vBw+Pj64ceMG+vXrJ3c4RNTH1NTUwGg0IigoCO7u7nKHQz2orWPPvNT92KZEJCfm+r6rvWNva27iFW8iIiIiIiIiO2LHm4iIiIiIiMiO2PEmIiIiIiIisiN2vImIiIiIiIjsiB1vIiIi6pTt27dLE81ERUUhJyenzbpHjhzB/fffj4EDB0Kj0SAsLAxvvPFGD0ZLREQkH7t1vG8nGe/duxeTJk3C4MGD0a9fP8TExODgwYP2Co2IyG4aGxvlDoF6WF895hkZGVi6dClefPFFFBQUYPz48UhISEBRUZHV+p6enli0aBG+/PJLnD17FqtWrcKqVavwzjvv9HDkRERd01e/9/uy7jjmdrmdWEZGBh5//HFs374d999/P3bu3Ildu3bh22+/RUBAgEX9pUuXQqfTwWAwoH///khLS8OmTZtw7Ngx3HPPPTa9Jm8xQkRyamxsxIULF+Dq6orBgwdDpVJBoVDIHRbZkRACJpMJv/zyCxoaGjBixAi4uPx2Pru356X77rsPkZGRePvtt6Wy3/3ud3jkkUfwyiuv2PQc06dPh6enJ/7xj3/YVL+3tykROTbm+r6no1wP2J6b7NLx7o5kHB4ejsTERLz88ss21WcyJiK5mUwmFBcXo6qqSu5QqAd5eHjAz88PKpXKrLw35yWTyQQPDw/861//wrRp06TyJUuWoLCwEIcPH+7wOQoKCpCQkID169djwYIFNr1ub25TInIOzPV9U1u5HrA9N7l1d1AmkwknTpzA8uXLzcrj4uKQm5tr03M0NjaioqICAwYMaLNObW0tamtrpcfl5eWdC5iIqJuoVCoEBASgvr4eDQ0NcodDPcDV1RVubm597opHWVkZGhoa4Ovra1bu6+uLkpKSdrf19/fHL7/8gvr6eqxZs6bdTjdzPRE5Gub6vqe7cn23d7y7koxbvP7666isrMTMmTPbrPPKK69g7dq1XYqViKi7KRQKKJVKKJVKuUMhsrvW/4QIITr8xyQnJwc3b95EXl4eli9fjpCQEMyaNctqXeZ6InJEzPXUGXabXK0zyRgAdu/ejTVr1iAjIwNDhgxps96KFStw48YNabl06VKXYyYiIqKODRo0CK6urhYn1EtLSy1OvLcWFBSEkSNHIjk5GcuWLcOaNWvarMtcT0REvUW3d7y7kowzMjIwf/58fPzxx3jggQfaratWq9GvXz+zhYiIiOxPpVIhKioKmZmZZuWZmZkYN26czc8jhDAbSt4acz0REfUW3T7U/NZkfOuEK5mZmZg6dWqb2+3evRvz5s3D7t278dBDD3V3WERERNSNUlJS8PjjjyM6OhoxMTF45513UFRUhKeffhpA09Xqy5cv48MPPwQAvPXWWwgICEBYWBiApvt6b9q0Cc8++6xs+0BERNRTur3jDdx+Mt69ezeeeOIJbNmyBWPHjpWulms0Gvj4+Nj0mi2Ts3PiFSIicgQt+cgONw9xCImJibh69SrWrVuH4uJiREREYP/+/QgMDAQAFBcXm93Tu7GxEStWrIDRaISbmxuCg4Px6quv4qmnnrL5NZnriYjI0dic74WdvPXWWyIwMFCoVCoRGRkpDh8+LK2bO3eu0Ov10mO9Xi8AWCxz5861+fUuXbpk9Tm4cOHChQsXOZdLly51Y3bt25jruXDhwoWLoy4d5Xu73MdbDo2Njbhy5Qq8vb27PNV7eXk5hg4dikuXLjnl78kYv/ycfR8Yv/ycfR8YPyCEQEVFBXQ6HVxc7DaXaZ/CXP8bZ48fcP59YPzyc/Z9YPzy6q74bc33dhlqLgcXFxf4+/t363M6+0QujF9+zr4PjF9+zr4PfT1+W38uRbZhrrfk7PEDzr8PjF9+zr4PjF9e3RG/Lfmep+CJiIiIiIiI7IgdbyIiIiIiIiI7YsfbCrVajdWrV0OtVssdSqcwfvk5+z4wfvk5+z4wfnJ0zn6MnT1+wPn3gfHLz9n3gfHLq6fj7zWTqxERERERERE5Il7xJiIiIiIiIrIjdryJiIiIiIiI7IgdbyIiIiIiIiI7YsebiIiIiIiIyI7Y8W5l+/btCAoKgru7O6KiopCTkyN3SFa98soruPfee+Ht7Y0hQ4bgkUcewblz58zqJCUlQaFQmC1jx46VKWJLa9assYhPq9VK64UQWLNmDXQ6HTQaDWJjY3HmzBkZIzY3bNgwi/gVCgWeeeYZAI7X/l9++SX+9Kc/QafTQaFQ4JNPPjFbb0t719bW4tlnn8WgQYPg6emJKVOm4KeffnKIfairq8MLL7yAkSNHwtPTEzqdDk888QSuXLli9hyxsbEWx+Wxxx6TPX7AtveMnMego/itfR4UCgVee+01qY6c7W/L96YzfA6oezDf9wzm+p7n7Pmeud5x2x9gru8KdrxvkZGRgaVLl+LFF19EQUEBxo8fj4SEBBQVFckdmoXDhw/jmWeeQV5eHjIzM1FfX4+4uDhUVlaa1YuPj0dxcbG07N+/X6aIrQsPDzeL79SpU9K6jRs3YvPmzdi2bRvy8/Oh1WoxadIkVFRUyBjxb/Lz881iz8zMBAA8+uijUh1Hav/KykqMHj0a27Zts7relvZeunQp9u3bhz179uDIkSO4efMmHn74YTQ0NMi+D1VVVTh58iReeuklnDx5Env37sX58+cxZcoUi7rJyclmx2Xnzp09EX6HxwDo+D0j5zHoKP5b4y4uLsb7778PhUKBGTNmmNWTq/1t+d50hs8BdR3zfc9iru9Zzp7vmesdt/0B5vouHQNBkt///vfi6aefNisLCwsTy5cvlyki25WWlgoA4vDhw1LZ3LlzxdSpU+ULqgOrV68Wo0ePtrqusbFRaLVa8eqrr0plNTU1wsfHR+zYsaOHIrw9S5YsEcHBwaKxsVEI4djtD0Ds27dPemxLe1+/fl0olUqxZ88eqc7ly5eFi4uLOHDgQI/F3qL1Plhz/PhxAUD8+OOPUplerxdLliyxb3A2sBZ/R+8ZRzoGtrT/1KlTxcSJE83KHKX9hbD83nTGzwF1DvN9z2Gul5ez53vm+iaO3P7M9bbjFe9mJpMJJ06cQFxcnFl5XFwccnNzZYrKdjdu3AAADBgwwKw8OzsbQ4YMQWhoKJKTk1FaWipHeG26cOECdDodgoKC8Nhjj+GHH34AABiNRpSUlJgdD7VaDb1e75DHw2Qy4Z///CfmzZsHhUIhlTt6+7ewpb1PnDiBuro6szo6nQ4REREOeUyAps+FQqFA//79zco/+ugjDBo0COHh4Xjuuecc5soK0P57xpmOwc8//4zPPvsM8+fPt1jnKO3f+nuzt34OyBzzfc9jrnccvfF7jrlePsz1t8et01v2MmVlZWhoaICvr69Zua+vL0pKSmSKyjZCCKSkpOAPf/gDIiIipPKEhAQ8+uijCAwMhNFoxEsvvYSJEyfixIkTUKvVMkbc5L777sOHH36I0NBQ/Pzzz1i/fj3GjRuHM2fOSG1u7Xj8+OOPcoTbrk8++QTXr19HUlKSVObo7X8rW9q7pKQEKpUKd9xxh0UdR/yM1NTUYPny5Zg9ezb69esnlc+ZMwdBQUHQarU4ffo0VqxYga+//loaPiinjt4zznQMPvjgA3h7e2P69Olm5Y7S/ta+N3vj54AsMd/3LOZ6x9LbvueY6+XFXH972PFu5dYzmEDTAWtd5mgWLVqEb775BkeOHDErT0xMlP6OiIhAdHQ0AgMD8dlnn1l8QOSQkJAg/T1y5EjExMQgODgYH3zwgTTJhLMcj/feew8JCQnQ6XRSmaO3vzWdaW9HPCZ1dXV47LHH0NjYiO3bt5utS05Olv6OiIjAiBEjEB0djZMnTyIyMrKnQzXT2feMIx6D999/H3PmzIG7u7tZuaO0f1vfm0Dv+RxQ+5wlv9zKGfM9c71j6g3fc8z18mOuvz0cat5s0KBBcHV1tTiLUVpaanFGxJE8++yz+PTTT5GVlQV/f/926/r5+SEwMBAXLlzooehuj6enJ0aOHIkLFy5IM546w/H48ccf8fnnn2PBggXt1nPk9relvbVaLUwmE65du9ZmHUdQV1eHmTNnwmg0IjMz0+wMuDWRkZFQKpUOeVxav2ec5Rjk5OTg3LlzHX4mAHnav63vzd70OaC2Md/Li7leXr3le465Xn7M9bePHe9mKpUKUVFRFkMgMjMzMW7cOJmiapsQAosWLcLevXvxxRdfICgoqMNtrl69ikuXLsHPz68HIrx9tbW1OHv2LPz8/KThKbceD5PJhMOHDzvc8UhLS8OQIUPw0EMPtVvPkdvflvaOioqCUqk0q1NcXIzTp087zDFpScQXLlzA559/joEDB3a4zZkzZ1BXV+eQx6X1e8YZjgHQdFUoKioKo0eP7rBuT7Z/R9+bveVzQO1jvpcXc728esP3HHO9Y2Cu71xw1GzPnj1CqVSK9957T3z77bdi6dKlwtPTU1y8eFHu0Cz85S9/ET4+PiI7O1sUFxdLS1VVlRBCiIqKCpGamipyc3OF0WgUWVlZIiYmRtx5552ivLxc5uibpKamiuzsbPHDDz+IvLw88fDDDwtvb2+pvV999VXh4+Mj9u7dK06dOiVmzZol/Pz8HCZ+IYRoaGgQAQEB4oUXXjArd8T2r6ioEAUFBaKgoEAAEJs3bxYFBQXSLKC2tPfTTz8t/P39xeeffy5OnjwpJk6cKEaPHi3q6+tl34e6ujoxZcoU4e/vLwoLC80+F7W1tUIIIb777juxdu1akZ+fL4xGo/jss89EWFiYuOeee3pkH9qL39b3jJzHoKP3kBBC3LhxQ3h4eIi3337bYnu527+j700hnONzQF3HfN9zmOt7nrPne+Z6x23/Fsz1ncOOdytvvfWWCAwMFCqVSkRGRprdrsORALC6pKWlCSGEqKqqEnFxcWLw4MFCqVSKgIAAMXfuXFFUVCRv4LdITEwUfn5+QqlUCp1OJ6ZPny7OnDkjrW9sbBSrV68WWq1WqNVqMWHCBHHq1CkZI7Z08OBBAUCcO3fOrNwR2z8rK8vqe2bu3LlCCNvau7q6WixatEgMGDBAaDQa8fDDD/foPrW3D0ajsc3PRVZWlhBCiKKiIjFhwgQxYMAAoVKpRHBwsFi8eLG4evWq7PHb+p6R8xh09B4SQoidO3cKjUYjrl+/brG93O3f0femEM7xOaDuwXzfM5jre56z53vmesdt/xbM9Z2jaA6QiIiIiIiIiOyAv/EmIiIiIiIisiN2vImIiIiIiIjsiB1vIiIiIiIiIjtix5uIiIiIiIjIjtjxJiIiIiIiIrIjdryJiIiIiIiI7IgdbyIiIiIiIiI7YsebiIiIiIiIyI7Y8SYiIiIiIiKyI3a8iYiIiIiIiOyIHW8iIiIiIiIiO2LHm4iIiIiIiMiO/j/4u9tfXq2PqQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = width_multiplier_1.keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(width_multiplier_1[field], label=\"Width-1\", linestyle=\"--\")\n",
    "    plt.plot(width_multiplier_75[field], label=\"Width-0.75\")\n",
    "    plt.plot(width_multiplier_50[field], label=\"Width-0.5\", linestyle=\"--\")\n",
    "    plt.plot(width_multiplier_25[field], label=\"Width-0.25\", linestyle=\"-\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
